# Comparative Analysis of Automated Text Summarization Techniques: The Case of Ethiopian Languages

Authors: Wubetu Barud Demilie
Journal: Wireless Communications and Mobile Computing
 (2022)

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## Abstract

Nowadays, there is an abundance of information available from both online and offline sources. For a single topic, we can get more than hundreds of sources containing a wealth of information. The ability to extract or generate a summary of popular content allows users to quickly search for content and obtain preliminary data in the shortest amount of time. Manually extracting useful information from them is a difficult task. Automatic text summarization (ATS) systems are being developed to address this issue. Text summarization is the process of extracting useful information from large documents and compressing it into a summary while retaining all the relevant contents. This review paper provides a broad overview of ATS research works in various Ethiopian languages such as Amharic, Afan Oromo, and Tigrinya using different text summarization approaches. The work has identified the novel and recommended state-of-the-art techniques and methods for future researchers in the area and provides knowledge and useful support to new researchers in this field by providing a concise overview of the various feature extraction methods and classification techniques required for different types of ATS approaches applied to the Ethiopian languages. Finally, different recommendations for future researchers have been forwarded.


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## Body

## 1. Introduction

Extracting important information from texts quickly and accurately has become a desirable task given the enormous amounts of digital text data generated every day. One solution to this issue is ATS. It is a strategy for making a document shorter while retaining important words and sentences without diluting its meaning or information [1–3]. It is a technique for creating an insightful summary with the aid of technology or computer-based tools. Even though, the earliest computer programs for summarizing text were developed in the 1950s [4, 5]. Accordingly, one of the most challenging areas of natural language processing (NLP) is ATS. Because a text’s syntactic and semantic structure makes it extremely difficult for machines to understand a text’s meaning in its entirety. Both humans and machines are capable of performing text summarization tasks accordingly. Summaries produced by computers frequently differ from those produced by humans [6]. A summarization system can be used to generate up-to-date data, run queries, or inform users about a certain topic.Accordingly, websites, user reviews, news, blogs, social media networks, and other digital resources are enormous sources of textual data. Additionally, the numerous archives of news stories, novels, books, legal documents, biomedical records, scientific papers, etc. include a richness of textual material. The amount of text in archives and on the Internet is constantly increasing. Users spend a lot of time searching for the information they need as a result. Even the linguistic element of search results is beyond their ability to read and understand. The generated texts contain a lot of redundant or insignificant content. Condensing and summarizing the text resources becomes urgent and significantly more crucial as a result. Manual text summarization is a time- and resource-intensive task and is expensive.Accordingly, the promising study field of ATS is being given a lot of attention from academics to various industries. Different text summarization approaches have been put forth for decades, including graph-based (GB) [7–10], deep learning (DL) [11–24], and machine learning (ML) [25, 26], and an analysis work has been done using graph and network, statistical, deep and machine learning, query, semantic, and sentence weighting based [27–29]. However, ATS is still one of the most challenging problems because of the complexity of input text(s) or document(s) [30]. In several disciplines, state-of-the-art prediction outcomes are currently developed using DL and ML approaches, which have become potent tools of NLP including self-driving cars, news aggregation, fraud news detection, virtual assistants, entertainment, visual recognition, healthcare, personalization, identifying developmental delays in children, coloring black-and-white images, adding sound to silent movies, creating automatic handwriting, playing games automatically, translating image languages, restoring pixels, providing photo descriptions, demographic and election predictions, DL, and automatic machine translation [31–34].Accordingly, an overview is required to better grasp what has been done in ATS including the implemented research works and approaches too. This review paper fills this gap by concentrating on general ATS tasks and attempting to shed light on some of the issues that naturally occur when using the current ATS approaches for Ethiopian languages. For this review work, the author compares the implemented ATS approaches including all the evaluation metrics that have been indicated quantitatively by the researchers.This review paper will have the following contributions:(i)
The analysis results provide an in-depth clarification of the topics/trends for ATS research(ii)
Provides a detailed reference to the datasets, preprocessing, and features that were used(iii)
Describes the techniques and methods that were frequently used by researchers as a means of comparison and method development(iv)
The work identifies novel and recommended state-of-the-art techniques and methods for future researchers in the field(v)
As indicated in the related work, future researchers can easily identify all the works that have been implemented for the Ethiopian and foreign languages(vi)
The researcher has examined the working mechanisms and applications of ATS in various domains(vii)
The analysis and comparison may aid in developing a better awareness of the advantages to readers of reading computer-generated summaries of research articles and papers accordingly(viii)
This work may also aid in understanding the limitations of the same, which arise primarily from the difficulties encountered in the realization of computer-generated summaries(ix)
At the end of this review paper, several recommendations for opportunities and challenges related to ATS have been forwardedThe rest of the paper is organized into different but interrelated subsections. The paper begins by discussing the structure of automated text summarization (SATS) in Structure of Automated Text Summarization (SATS), ATS techniques/approaches in ATS Techniques/Approaches, ATS algorithms in the ATS Algorithms, related works in Related Works, performance evaluation of ATS in Performance Evaluation of ATS, results and discussions in Results and Discussions, and conclusion and recommendation in Conclusion and Recommendation.


## 2. Structure of Automated Text Summarization (SATS)

The fundamental structure of ATS is divided into various components, including an input layer with two distinct types of documents: single and multiple documents. A single document summarizing (SDS) method chooses the key phrases from the source text while taking the summary’s word count into account [35, 36]. In contrast, many documents are picked as input for a multiple document summarization (MDS), which results in a summary [37–39]. Preprocessing, feature extraction, application of a summary generating technique, or methods to summarize the source material are typically the stages that an ATS goes through. The monolithic structure of the document is used in a single document summary system; however, it is less important in multiple document summarization systems. The primary problem with MDS is from the data’s collection from a large number of sources, some of which may include more redundant information than is generally found in a single document. An illustration of the structure and steps of an ATS is presented in Figure 1.Figure 1
The structure of an ATS system.As indicated in Figure1, the image depicts the steps in which the input (single or multidocument) is preprocessed before feature extraction. The following section discusses the preconditions before conducting a document summary.


### 2.1. Preprocessing

Sentence segmentation, punctuation mark removal, stop-word filtering, stemming (reducing common root words), and other critical techniques are used to preprocess incoming text documents during the preprocessing phase. Before summary, the raw source document is cleaned up and converted to a more palatable data format. To clear the loud and unfiltered text, several preprocessing steps are taken. The methods listed below are some of the preprocessing techniques most frequently used for different languages accordingly [26, 28, 40]:
(1)
Parts of speech tagging (POST): it is a method of categorizing or classifying text words into different speech categories, such as nouns, verbs, adverbs, and adjectives(2)
Stop word removal: stop words are eliminated either before or after textual analysis, depending on the context. They should be identified and removed from plain text(3)
Stemming: it strips a group of words designated as primary or root forms of their inflections and derivative forms. Text stemming changes words to take into account various word forms by utilizing linguistic techniques like affixation (i.e., prefixes, infixes, and suffixes)(4)
Named entity recognition (NER): words in the input text that identify an item by name (such as a person’s name, a place’s name, and a company’s name, etc.) are detected(5)
Tokenization: by breaking up text flows into tokens which can be words, phrases, symbols, or other meaningful units, it examines the words in a document(6)
Capitalization: it is necessary to change every letter in a document to lowercase letters (but, it depends on the nature of languages) because different capitalizations in different papers can be problematic. This technique then unifies all text and document words into a single feature(7)
Slang and abbreviation: the preprocessing stage deals with two distinct kinds of text anomalies: slang and abbreviations(8)
Noise removal: punctuation and special characters are among the many additional characters found in most textual material(9)
Spelling correction: the preprocessing procedure includes an optional step for spelling correction. Texts and documents frequently contain typos, especially online media text collections (social media platforms such as Twitter and Facebook)(10)
Lemmatization: it is the process of replacing a word’s suffix with a new one or removing a word’s suffix to achieve the core word form (lemma)


### 2.2. Feature Extraction

By choosing various elements from the original text, sentences must be extracted, which is essential for the complete summary process. In the feature extraction phase, specific features are applied to each sentence, and the best-scoring sentences are chosen for the summary. Finding topic expressions, important data features, or attributes from the source documents is a process known as feature extraction. Extraction of features and text representation method are the two steps that the ATS takes to find the key sentences in the text. For creating sentences for text summary, it is the extraction of features and text representation techniques that are most frequently utilized. It consists the following [28, 40].


#### 2.2.1. Features

The initial step in the feature extraction process is gathering the necessary features. To extract a crucial sentence from a document, the phrases must be represented as vectors or scored. Following are the most common characteristics used to determine a sentence’s score and how closely it resembles a summary:(1)
Term frequency (TF): the relevance of terms in a single document is assessed using the TF metric. It is frequently used to express a word’s weight because it is one of the most fundamental ATS features(2)
Term frequency-inverse sentence frequency (TFISF): the term frequency-inverse sentence frequency among the sentences in all documents is measured by the most relevant feature extraction method based on the text summarization survey. This technique is used to construct the weights, which appear to be reasonable indicators for meaningful sentences(3)
Position feature: the first and last sentences are typically seen to have more information about the work. This increases the likelihood that researchers will be mentioned in the summary. The position can be denoted by binary values 0 and 1 accordingly. The binary or regressive score for the feature could be any value between 0 and 1(4)
Length feature: the length of a sentence can reveal if it is suitable for summarization. But, it might not be wise to judge a sentence’s importance based solely on how long it is. Very long and comparatively short sentences are typically excluded from the summary because of their size with other sentences in the source(5)
Sentence with sentence similarity: summarization may be aided by the questioning sentences’ similarity to other sentences in the text. There are several approaches to carrying out this feature extraction process(6)
Title feature (TiF): sentences containing terms from the headline may suggest the document’s theme and are more likely to be included in the summary(7)
Phrasal information (PI): when summarizing, the ratio of terms is always useful. Adjective phrases, noun phrases, prepositional phrases, and verbal phrases are all included in the phrase collection(8)
Title similarity (TS): if a sentence shares the most words with the title, it is given a passing grade. The overall number of words and the number of words in each sentence that appears in the title can both be determined using the word count(9)
Sentence position (SP): this function establishes the placement of a sentence within the text. The placement of the sentences in the text whether they are the first sentences of a paragraph or not determines their significance(10)
Thematic word (TW): this attribute is linked to terms that are domain-specific and commonly appear in texts that are most likely pertinent to the subject matter of the document. The amount of theme words in the phrase is compared to the maximum sum of thematic keywords in the sentence to determine the score(11)
Numerical data (ND): in general, it is important to make a statement that includes numerical facts. This is most often located in a document’s summary. By dividing the numerical information in a sentence by its length, the score is determined


#### 2.2.2. Text Representation

The input documents are represented more accurately by using the text representation models. Text representation techniques in NLP entail turning words into numbers so that computers can understand and decode linguistic patterns. These methods typically create a link between the selected phrase and the document’s context word. The following are some examples of well-liked text presentation techniques [28, 40]:
(1)
N-gram: the N-gram method is perfect for operations involving multiple languages, because it does not require any linguistic preparation. A group of words or characters with N components is known as an N-gram. The text can be represented by a vector, which is typical of a decent size, and this model is straightforward to construct. A collection of text N-grams are made up of unigrams, bigrams, trigrams, quadgrams, and other N-grams. The N-gram has various drawbacks, such as the fact that the model is more accurate the higher theN value. These results, however, undergo a great deal of processing, necessitating a lot of RAM processing capacity. As the model is predicated on the chance of concepts occurring together, N-grams are also a sparse representation of languages. In the training corpus, any absent word is given a chance of zero(2)
Bag of words (BoW): the BoW model is the simplest type of numerical text representation. A bag of words vector can be used to express a phrase, such as a term itself. It is a condensed and streamlined version of a sentence’s main idea in a written document. The technique is applied to CV, NLP, Bayesian spam filters, document categorization, and ML-based information retrieval(3)
Term frequency-inverse document frequency (TFIDF: the IDF determines the importance of a word, whereas the term frequency (TF) determines how frequently a term appears in a text. The IDF value is required because simply computing the TF is insufficient for understanding the meaning of words. The combination of TF and IDF is known as the term frequency-inverse document frequency (TF-IDF). However, TF-IDF has several drawbacks, including the fact that it directly calculates text similarity in the word-count space, which can be slow with large vocabularies. Furthermore, it is assumed that the counts of different terms provide independent evidence of similarities(4)
Word embedding: one kind of feature learning is word embedding. A lexicon maps every word or phrase to an N-dimensional vector of absolute values. To turn N-grams into understandable inputs for ML systems, several word embedding techniques have been developed. Three of the most popular DL techniques for word embedding are examined in this study: Word2Vec, GloVe, and FastText [40]
(i)
Word2Vec: a method for making embedding is called Word2Vec. Two methods to get it (both using NN) are skip-gram and a common bag of words (CBOW). The CBOW technique makes an effort to predict the word that will go with each word by using its context as an input. Instead of anticipating the current word based on context, skip-gram seeks to maximize the classification of a word based on another word in the exact phrase(ii)
Global vectors for word representation (GloVe): another effective method of word embedding that has been used for text categorization is GloVe. The Word2Vec procedure is comparable to this approach. With the help of a vast corpus and the surrounding words, each word is represented by a high-dimensional vector(iii)
FastText: the morphology of words is disregarded by several alternative word-embedding representations


## 2.1. Preprocessing

Sentence segmentation, punctuation mark removal, stop-word filtering, stemming (reducing common root words), and other critical techniques are used to preprocess incoming text documents during the preprocessing phase. Before summary, the raw source document is cleaned up and converted to a more palatable data format. To clear the loud and unfiltered text, several preprocessing steps are taken. The methods listed below are some of the preprocessing techniques most frequently used for different languages accordingly [26, 28, 40]:
(1)
Parts of speech tagging (POST): it is a method of categorizing or classifying text words into different speech categories, such as nouns, verbs, adverbs, and adjectives(2)
Stop word removal: stop words are eliminated either before or after textual analysis, depending on the context. They should be identified and removed from plain text(3)
Stemming: it strips a group of words designated as primary or root forms of their inflections and derivative forms. Text stemming changes words to take into account various word forms by utilizing linguistic techniques like affixation (i.e., prefixes, infixes, and suffixes)(4)
Named entity recognition (NER): words in the input text that identify an item by name (such as a person’s name, a place’s name, and a company’s name, etc.) are detected(5)
Tokenization: by breaking up text flows into tokens which can be words, phrases, symbols, or other meaningful units, it examines the words in a document(6)
Capitalization: it is necessary to change every letter in a document to lowercase letters (but, it depends on the nature of languages) because different capitalizations in different papers can be problematic. This technique then unifies all text and document words into a single feature(7)
Slang and abbreviation: the preprocessing stage deals with two distinct kinds of text anomalies: slang and abbreviations(8)
Noise removal: punctuation and special characters are among the many additional characters found in most textual material(9)
Spelling correction: the preprocessing procedure includes an optional step for spelling correction. Texts and documents frequently contain typos, especially online media text collections (social media platforms such as Twitter and Facebook)(10)
Lemmatization: it is the process of replacing a word’s suffix with a new one or removing a word’s suffix to achieve the core word form (lemma)


## 2.2. Feature Extraction

By choosing various elements from the original text, sentences must be extracted, which is essential for the complete summary process. In the feature extraction phase, specific features are applied to each sentence, and the best-scoring sentences are chosen for the summary. Finding topic expressions, important data features, or attributes from the source documents is a process known as feature extraction. Extraction of features and text representation method are the two steps that the ATS takes to find the key sentences in the text. For creating sentences for text summary, it is the extraction of features and text representation techniques that are most frequently utilized. It consists the following [28, 40].


### 2.2.1. Features

The initial step in the feature extraction process is gathering the necessary features. To extract a crucial sentence from a document, the phrases must be represented as vectors or scored. Following are the most common characteristics used to determine a sentence’s score and how closely it resembles a summary:(1)
Term frequency (TF): the relevance of terms in a single document is assessed using the TF metric. It is frequently used to express a word’s weight because it is one of the most fundamental ATS features(2)
Term frequency-inverse sentence frequency (TFISF): the term frequency-inverse sentence frequency among the sentences in all documents is measured by the most relevant feature extraction method based on the text summarization survey. This technique is used to construct the weights, which appear to be reasonable indicators for meaningful sentences(3)
Position feature: the first and last sentences are typically seen to have more information about the work. This increases the likelihood that researchers will be mentioned in the summary. The position can be denoted by binary values 0 and 1 accordingly. The binary or regressive score for the feature could be any value between 0 and 1(4)
Length feature: the length of a sentence can reveal if it is suitable for summarization. But, it might not be wise to judge a sentence’s importance based solely on how long it is. Very long and comparatively short sentences are typically excluded from the summary because of their size with other sentences in the source(5)
Sentence with sentence similarity: summarization may be aided by the questioning sentences’ similarity to other sentences in the text. There are several approaches to carrying out this feature extraction process(6)
Title feature (TiF): sentences containing terms from the headline may suggest the document’s theme and are more likely to be included in the summary(7)
Phrasal information (PI): when summarizing, the ratio of terms is always useful. Adjective phrases, noun phrases, prepositional phrases, and verbal phrases are all included in the phrase collection(8)
Title similarity (TS): if a sentence shares the most words with the title, it is given a passing grade. The overall number of words and the number of words in each sentence that appears in the title can both be determined using the word count(9)
Sentence position (SP): this function establishes the placement of a sentence within the text. The placement of the sentences in the text whether they are the first sentences of a paragraph or not determines their significance(10)
Thematic word (TW): this attribute is linked to terms that are domain-specific and commonly appear in texts that are most likely pertinent to the subject matter of the document. The amount of theme words in the phrase is compared to the maximum sum of thematic keywords in the sentence to determine the score(11)
Numerical data (ND): in general, it is important to make a statement that includes numerical facts. This is most often located in a document’s summary. By dividing the numerical information in a sentence by its length, the score is determined


### 2.2.2. Text Representation

The input documents are represented more accurately by using the text representation models. Text representation techniques in NLP entail turning words into numbers so that computers can understand and decode linguistic patterns. These methods typically create a link between the selected phrase and the document’s context word. The following are some examples of well-liked text presentation techniques [28, 40]:
(1)
N-gram: the N-gram method is perfect for operations involving multiple languages, because it does not require any linguistic preparation. A group of words or characters with N components is known as an N-gram. The text can be represented by a vector, which is typical of a decent size, and this model is straightforward to construct. A collection of text N-grams are made up of unigrams, bigrams, trigrams, quadgrams, and other N-grams. The N-gram has various drawbacks, such as the fact that the model is more accurate the higher theN value. These results, however, undergo a great deal of processing, necessitating a lot of RAM processing capacity. As the model is predicated on the chance of concepts occurring together, N-grams are also a sparse representation of languages. In the training corpus, any absent word is given a chance of zero(2)
Bag of words (BoW): the BoW model is the simplest type of numerical text representation. A bag of words vector can be used to express a phrase, such as a term itself. It is a condensed and streamlined version of a sentence’s main idea in a written document. The technique is applied to CV, NLP, Bayesian spam filters, document categorization, and ML-based information retrieval(3)
Term frequency-inverse document frequency (TFIDF: the IDF determines the importance of a word, whereas the term frequency (TF) determines how frequently a term appears in a text. The IDF value is required because simply computing the TF is insufficient for understanding the meaning of words. The combination of TF and IDF is known as the term frequency-inverse document frequency (TF-IDF). However, TF-IDF has several drawbacks, including the fact that it directly calculates text similarity in the word-count space, which can be slow with large vocabularies. Furthermore, it is assumed that the counts of different terms provide independent evidence of similarities(4)
Word embedding: one kind of feature learning is word embedding. A lexicon maps every word or phrase to an N-dimensional vector of absolute values. To turn N-grams into understandable inputs for ML systems, several word embedding techniques have been developed. Three of the most popular DL techniques for word embedding are examined in this study: Word2Vec, GloVe, and FastText [40]
(i)
Word2Vec: a method for making embedding is called Word2Vec. Two methods to get it (both using NN) are skip-gram and a common bag of words (CBOW). The CBOW technique makes an effort to predict the word that will go with each word by using its context as an input. Instead of anticipating the current word based on context, skip-gram seeks to maximize the classification of a word based on another word in the exact phrase(ii)
Global vectors for word representation (GloVe): another effective method of word embedding that has been used for text categorization is GloVe. The Word2Vec procedure is comparable to this approach. With the help of a vast corpus and the surrounding words, each word is represented by a high-dimensional vector(iii)
FastText: the morphology of words is disregarded by several alternative word-embedding representations


## 2.2.1. Features

The initial step in the feature extraction process is gathering the necessary features. To extract a crucial sentence from a document, the phrases must be represented as vectors or scored. Following are the most common characteristics used to determine a sentence’s score and how closely it resembles a summary:(1)
Term frequency (TF): the relevance of terms in a single document is assessed using the TF metric. It is frequently used to express a word’s weight because it is one of the most fundamental ATS features(2)
Term frequency-inverse sentence frequency (TFISF): the term frequency-inverse sentence frequency among the sentences in all documents is measured by the most relevant feature extraction method based on the text summarization survey. This technique is used to construct the weights, which appear to be reasonable indicators for meaningful sentences(3)
Position feature: the first and last sentences are typically seen to have more information about the work. This increases the likelihood that researchers will be mentioned in the summary. The position can be denoted by binary values 0 and 1 accordingly. The binary or regressive score for the feature could be any value between 0 and 1(4)
Length feature: the length of a sentence can reveal if it is suitable for summarization. But, it might not be wise to judge a sentence’s importance based solely on how long it is. Very long and comparatively short sentences are typically excluded from the summary because of their size with other sentences in the source(5)
Sentence with sentence similarity: summarization may be aided by the questioning sentences’ similarity to other sentences in the text. There are several approaches to carrying out this feature extraction process(6)
Title feature (TiF): sentences containing terms from the headline may suggest the document’s theme and are more likely to be included in the summary(7)
Phrasal information (PI): when summarizing, the ratio of terms is always useful. Adjective phrases, noun phrases, prepositional phrases, and verbal phrases are all included in the phrase collection(8)
Title similarity (TS): if a sentence shares the most words with the title, it is given a passing grade. The overall number of words and the number of words in each sentence that appears in the title can both be determined using the word count(9)
Sentence position (SP): this function establishes the placement of a sentence within the text. The placement of the sentences in the text whether they are the first sentences of a paragraph or not determines their significance(10)
Thematic word (TW): this attribute is linked to terms that are domain-specific and commonly appear in texts that are most likely pertinent to the subject matter of the document. The amount of theme words in the phrase is compared to the maximum sum of thematic keywords in the sentence to determine the score(11)
Numerical data (ND): in general, it is important to make a statement that includes numerical facts. This is most often located in a document’s summary. By dividing the numerical information in a sentence by its length, the score is determined


## 2.2.2. Text Representation

The input documents are represented more accurately by using the text representation models. Text representation techniques in NLP entail turning words into numbers so that computers can understand and decode linguistic patterns. These methods typically create a link between the selected phrase and the document’s context word. The following are some examples of well-liked text presentation techniques [28, 40]:
(1)
N-gram: the N-gram method is perfect for operations involving multiple languages, because it does not require any linguistic preparation. A group of words or characters with N components is known as an N-gram. The text can be represented by a vector, which is typical of a decent size, and this model is straightforward to construct. A collection of text N-grams are made up of unigrams, bigrams, trigrams, quadgrams, and other N-grams. The N-gram has various drawbacks, such as the fact that the model is more accurate the higher theN value. These results, however, undergo a great deal of processing, necessitating a lot of RAM processing capacity. As the model is predicated on the chance of concepts occurring together, N-grams are also a sparse representation of languages. In the training corpus, any absent word is given a chance of zero(2)
Bag of words (BoW): the BoW model is the simplest type of numerical text representation. A bag of words vector can be used to express a phrase, such as a term itself. It is a condensed and streamlined version of a sentence’s main idea in a written document. The technique is applied to CV, NLP, Bayesian spam filters, document categorization, and ML-based information retrieval(3)
Term frequency-inverse document frequency (TFIDF: the IDF determines the importance of a word, whereas the term frequency (TF) determines how frequently a term appears in a text. The IDF value is required because simply computing the TF is insufficient for understanding the meaning of words. The combination of TF and IDF is known as the term frequency-inverse document frequency (TF-IDF). However, TF-IDF has several drawbacks, including the fact that it directly calculates text similarity in the word-count space, which can be slow with large vocabularies. Furthermore, it is assumed that the counts of different terms provide independent evidence of similarities(4)
Word embedding: one kind of feature learning is word embedding. A lexicon maps every word or phrase to an N-dimensional vector of absolute values. To turn N-grams into understandable inputs for ML systems, several word embedding techniques have been developed. Three of the most popular DL techniques for word embedding are examined in this study: Word2Vec, GloVe, and FastText [40]
(i)
Word2Vec: a method for making embedding is called Word2Vec. Two methods to get it (both using NN) are skip-gram and a common bag of words (CBOW). The CBOW technique makes an effort to predict the word that will go with each word by using its context as an input. Instead of anticipating the current word based on context, skip-gram seeks to maximize the classification of a word based on another word in the exact phrase(ii)
Global vectors for word representation (GloVe): another effective method of word embedding that has been used for text categorization is GloVe. The Word2Vec procedure is comparable to this approach. With the help of a vast corpus and the surrounding words, each word is represented by a high-dimensional vector(iii)
FastText: the morphology of words is disregarded by several alternative word-embedding representations


## 3. ATS Techniques/Approaches

Choosing an effective approach is the first and most important step in the summarizing strategy. Some techniques involve picking out the most important phrases and sentences from the texts, while others call for compressing and paraphrasing a single statement. In general, ATS is a difficult and time-consuming process that frequently produces subpar results to computers’ inadequate comprehension of human language. For summary texts, researchers have tried to extract them within improved performances and standardized classifications. Depending on the number of input documents, there are many ways to text summarization, such as single or multiple, objectively generic, domain-specific, query-based, and performance-wise. Accordingly, performance-wise analysis is classified into the following main classes [26, 28, 40].


### 3.1. Extractive Text Summarization

Extractive-based ATS involves selecting a set of sentences or phrases in the text based on the scores they earn according to a given criterion and copying them into the summary without any change [41, 42]. Figure 2 illustrates the working principles of extractive summarization. The goal of the extractive-based ATS is to locate keywords and sentences within a text source and use them to efficiently produce a summary. To do this, important sentences from the original text must be chosen. A subset of the words from the original document is created by using these key sentences to duplicate the text’s fundamental components word for word. Three separate responsibilities make up this foundation [40]:
(1)
Splitting: splitting the original document into sentences and then producing a draft version of the text that emphasizes the tasks(2)
Assigning: depending on how well they perform after the production of the representation, each sentence is given a score that indicates its value. Scores for topic representations are based on the topic words in the text. Conversely, indicator representation scores are determined by the sentence’s characteristics(3)
Selecting: select the highest-scoring sentences to form the summary. Two ML approaches are used in extractive text summarization: supervised and unsupervised ML(i)
Supervised ML methods: in supervised ML approaches, the initial step is to train the system to recognize summarized and nonsummarized texts to learn how to classify documents. These approaches’ ML and NN algorithms need a labeled dataset for training that contains both summarized and nonsummarized texts(ii)
Unsupervised ML methods: the summarizing process can be carried out using unsupervised ML techniques without any assistance, such as the user selecting the document’s introductory sentencesFigure 2
Extractive-based ATS.To take user input and operate automatically, these methods just need sophisticated algorithms like GB, concept-based, fuzzy logic, and latent semantics. Accordingly, for large amounts of data, these methods are advantageous. The extractive-based ATS can be depicted in Figure2.


### 3.2. Abstractive Text Summarization

Abstractive-based ATS means producing a brief interpretation of the original text [14, 41, 43]. In this method, sentences of the summary may not necessarily be written in the same way as in the original text. The summarized sentences may or may not exist in the original text. Figure 3 illustrates the working principles of abstractive-based ATS.Figure 3
Abstractive-based ATS.The development and automation of the conventional approach to text summarization are known as abstractive-based ATS. The abstractive process paraphrases a text’s major passages and main concepts to identify them. The following are some typical steps that make up the abstractive summarization process [40]:
(1)
Using a vocabulary set different from the source to analyze the text documents’ primary contents(2)
Using NLP models to summarize the pertinent information that fits the semantics and contains all the key points of the original document. Two different types of abstractive summarization methods exist a structure-based method and a semantic-based one(i)
Structure-based methods: by using abstract or cognitive algorithms, the structure-based approach continuously filters the most important information from documents. The most popular algorithms are those for rule-based, template-based, and tree-based ontologies(ii)
Semantic-based methods: sing NLP on the full manuscript, the semantic-based approach aims to improve the sentences. Accordingly, by using some techniques, this methodology can quickly locate the noun and verb phrases. The abstractive-based ATS can be depicted in Figure3


## 3.1. Extractive Text Summarization

Extractive-based ATS involves selecting a set of sentences or phrases in the text based on the scores they earn according to a given criterion and copying them into the summary without any change [41, 42]. Figure 2 illustrates the working principles of extractive summarization. The goal of the extractive-based ATS is to locate keywords and sentences within a text source and use them to efficiently produce a summary. To do this, important sentences from the original text must be chosen. A subset of the words from the original document is created by using these key sentences to duplicate the text’s fundamental components word for word. Three separate responsibilities make up this foundation [40]:
(1)
Splitting: splitting the original document into sentences and then producing a draft version of the text that emphasizes the tasks(2)
Assigning: depending on how well they perform after the production of the representation, each sentence is given a score that indicates its value. Scores for topic representations are based on the topic words in the text. Conversely, indicator representation scores are determined by the sentence’s characteristics(3)
Selecting: select the highest-scoring sentences to form the summary. Two ML approaches are used in extractive text summarization: supervised and unsupervised ML(i)
Supervised ML methods: in supervised ML approaches, the initial step is to train the system to recognize summarized and nonsummarized texts to learn how to classify documents. These approaches’ ML and NN algorithms need a labeled dataset for training that contains both summarized and nonsummarized texts(ii)
Unsupervised ML methods: the summarizing process can be carried out using unsupervised ML techniques without any assistance, such as the user selecting the document’s introductory sentencesFigure 2
Extractive-based ATS.To take user input and operate automatically, these methods just need sophisticated algorithms like GB, concept-based, fuzzy logic, and latent semantics. Accordingly, for large amounts of data, these methods are advantageous. The extractive-based ATS can be depicted in Figure2.


## 3.2. Abstractive Text Summarization

Abstractive-based ATS means producing a brief interpretation of the original text [14, 41, 43]. In this method, sentences of the summary may not necessarily be written in the same way as in the original text. The summarized sentences may or may not exist in the original text. Figure 3 illustrates the working principles of abstractive-based ATS.Figure 3
Abstractive-based ATS.The development and automation of the conventional approach to text summarization are known as abstractive-based ATS. The abstractive process paraphrases a text’s major passages and main concepts to identify them. The following are some typical steps that make up the abstractive summarization process [40]:
(1)
Using a vocabulary set different from the source to analyze the text documents’ primary contents(2)
Using NLP models to summarize the pertinent information that fits the semantics and contains all the key points of the original document. Two different types of abstractive summarization methods exist a structure-based method and a semantic-based one(i)
Structure-based methods: by using abstract or cognitive algorithms, the structure-based approach continuously filters the most important information from documents. The most popular algorithms are those for rule-based, template-based, and tree-based ontologies(ii)
Semantic-based methods: sing NLP on the full manuscript, the semantic-based approach aims to improve the sentences. Accordingly, by using some techniques, this methodology can quickly locate the noun and verb phrases. The abstractive-based ATS can be depicted in Figure3


## 4. ATS Algorithms

Text summarization can be defined more precisely in terms of algorithms or methodologies. To produce a more accurate version of the summary text, numerous techniques and methodologies are used. The supervised, unsupervised, structured, semantic, extractive, abstractive, GB, and DL-based ATS methods and algorithms can be described as follows [26, 28, 40, 41].


### 4.1. Unsupervised Learning Methods

Unsupervised extractive text summarization, which is more like standard ATS, selects key sentences from documents. This algorithm chooses sentences without using labeled summaries during the training of either a text or dataset. Those techniques are also efficient because they do not need user input or human overviews to identify the document’s key components. Furthermore, unsupervised methods are effective because they do not require user feedback or human overviews to determine the document’s essential features. Unsupervised text summarization is more efficient than supervised text summarization and is better suited to long text summaries. The following are some unsupervised techniques or methods for summarizing texts or documents:(1)
Fuzzy logic (FL): the choice of fuzzy rules and membership functions is often part of the design process for a fuzzy logic method. A fuzzifier, inference engine, defuzzifier, and knowledge base are its four constituent parts. The most crucial passage from the source text or context is also chosen using a fuzzy logic method. However, for the fuzzy logic method to produce better outcomes, redundancy elimination must be used(2)
Concept-based method: the concept-based method takes concepts from the source content and reduces repetition by using similarity measurements. These retrieved concepts are calculated, and the sentences are scored based on importance. The constraints of both this method and fuzzy logic are the same, but fuzzy logic is superior since it can handle ambiguous circumstances better(3)
Latent semantic analysis (LSA) method: an algebraic statistical method called LSA can be used to uncover sentences’ hidden semantic components. The LSA is an unsupervised learning method that extracts knowledge and related terms from input texts. The benefit of this strategy is that it may be used without any external training or template to identify comparable terms that appear in different phrases. However, the LSA approach has some limitations, such as not examining word order, syntactic relations, or morphologies. Furthermore, it does not rely on outside information or sources other than the information found in the input documentFinally, this method is not comparable due to flaws such as performance degradation when using inhomogeneous datasets. Additionally, the semantic summary of texts benefits greatly from the use of this strategy.


### 4.2. Supervised Learning Methods

Sentence-level classifiers that learn to differentiate between summarized and nonsummarized sentences are known as supervised learning methods. Summaries written by humans and collections of documents can both learn the traits of the sentences they contain. In addition, it has substantial limitations in producing context summaries manually and requires more labeled training samples for classification [28, 40].
(1)
ML method: the ML method is used to classify the sentences as summary or nonsummary classes using training data. When extractive summaries are needed for numerous document copies, several techniques are used. Notably, each document’s sentence is depicted in this case as vectors. The ML methods are mostly applied to a set of training datasets that contain trainable texts. In the training phase, a variety of training manuals are fed into input documents and categorized according to the sentence weight. Although a straightforward regression model frequently outperforms classifiers, it still needs a large amount of training data whether an NN model has multiple layers that depend only on the data which is the only layer in the Ml models. Because of this, NN models are improving in usability and user appeal on ATS. Other common preprocessing information retrieval procedures, like stop-word removal, case folding, and stemming, are also included in the ML methods(2)
NN method: a three-layered feedforward network is used in a NN approach to learning the features of words during model training. The phase of feature matching is important, and some phases identify the relationship between the qualities. The steps to define meaningful sentences are to eliminate infrequent features, combine frequent elements, and then rank the phrases. As the network learns from its training data, the NN approach is also utilized to train according to the human’s preferences or needs. As a more complex variant of ML, NN algorithms outperform ML algorithms when there are more layers and hidden layers added. Hence, the RankNet approach, which was created in, likewise uses neural nets to automatically classify the pertinent sentences in the text. It uses a two-layer backpropagation NN that was trained using the RankNet technique(3)
Conditional random field (CRF) method: the CRFs are ML-based statistical modeling techniques that offer a consistent prediction. To extract the appropriate features, CRF employs nonnegative matrix factorization (NMF) techniques. The document’s introduction sentence is then defined using the appropriate components. By providing a more accurate representation of sentences and sections, CRF’s key advantage is distinguishing appropriate features. This technique has a major flaw in that it is domain-specific, necessitating the use of an external domain-specific framework throughout the training phase. Without initially establishing a time-consuming domain foundation, this methodology can be universally applied to any text. As a result, algorithms based on ML and NN are still preferable. These techniques rely on optimization, statistical analysis, topic selection, sentence centrality, or clustering. Because the optimization method requires a lot of processing and is expensive, researchers used a genetic algorithm to determine the best weights. It is necessary to specify the necessary number of iterationsTo generate abstractive summaries, language generation and compression strategies are required. Abstractive text summarization is also divided into two categories to produce better abstractive summaries: structured and semantic [40].


### 4.3. Structure-Based Methods

The original document calls for newly created sentences to summarize in the abstractive summary. The structure-based method interprets phrases from source materials according to a predetermined framework without distorting their original meaning. Structure-based methods, such as templates, tree-based, ontology-based, and rule-based structures, mostly rely on predefined forms and spatial reasoning schemas [28, 40].
(1)
Tree-based method: to produce an abstractive summary, the tree-based method first identifies sentences that share common knowledge and facts before combining them. Tree linearization, which derives from several dependency trees, is the name of this structure that resembles a tree. Dependency trees serve as a visual representation of a document’s source text. A syntactic tree is used by the tree-based approach to analyze various documents and locate common information. These techniques likewise result in fewer repetitive summaries, but they are unable to identify sentence relationships in isolation from context. As a result, it ignores important textual sentences. The method’s constant emphasis on syntax rather than semantics is another drawback. This approach stands out in structure-based methodologies despite these problems because of its fluency in summarizing(2)
Template-based method: by identifying similarities with a template space, the topic or content is extracted into possible phrases and speakers in the template-based methods. When a document needs a predetermined guideline or a custom template for the summary, the template-based method is employed. This process creates detailed summaries that are coherent and depending on the option; the content’s speakers and phrases are chosen. The problem with this strategy is that there is not much variability in the summaries because the templates for summaries are always predefined. Consequently, in contrast to the tree-based technique, it is unable to provide fluent summaries(3)
Rule-based method: through investigating, the rule-based technique uncovers information and assessments of key ideas in source documents. For example, what are the topics under interrogation and questioning? Answering these questions seeks to generate an abstractive summary, such as “What is the time-being of the story or topic?” and “What are the names of the people on the table, according to certain rules?”. When input documents need to be represented as classes and lists of aspects, rule-based approaches are employed instead of query-based ones. The creation of the regulations requires the use of this procedure, which takes time. This method is less effective than the other ways stated before in this paragraph due to the manually written rules(4)
Ontology-based method: a knowledge-based strategy called ontology serves as a formal name and characterization of the many entity kinds inside a given domain. This technique uses a foundation of information to enhance the effectiveness of summarization. When a document has a knowledge structure or is regularly built around the same topic, ontology-based methods excel. To provide coherent summaries, this strategy concentrates on documents that are specifically connected to a given area. This approach takes time, just like the rule-based approach does


### 4.4. Semantic-Based Methods

With a strong emphasis on noun and verb phrase identification, semantic-based approaches translate the linguistics of a document’s contents into a natural language generation (NLG) system. Less redundant and grammatically correct sentences can be created using these techniques. These methods have the drawback of occasionally ignoring important information or data, even though it is grammatically correct. This method includes the following [40]:
(1)
Multimodal semantic method: to comprehend both visual and word concepts from a document, the multimodal-based method is applied. To communicate text and images/pictures in multimodal materials, the multimodal semantic model collects ideas and creates relationships between them. Knowledge representation based on objects is the basis of a semantic model based on nodes and connections. Nodes represent concepts, whereas connections show the links between concepts. The information density measure is used to assess an expression’s completeness, relationships with other expressions, and repeats. Finally, ideas that summarize the selected concepts are created(2)
Semantic GB method: the semantic GB approach condenses a document by creating a rich semantic graph (RSG) for the original document and then condensing the resulting semantic graph. This method’s strength is its ability to produce succinct, coherent, and grammatically sound sentences using smaller networks. By giving weights to the nodes and edges of phrases, the semantic-graph-based model primarily extracts semantic information. Because of this, this approach generally performs well, but it does necessitate a semantic representation of the text(3)
Information item method: instead of creating an abstract from the words in the text file, the information item-based method summarizes a text file based on its abstract. In this method, a summary is created using an abstract representation of the source information. An information item is a minor part of a source document. The approach then extracts information from a text that should have a logical flow of information. An approach that is based on information items yields summaries that are shorter and less repetitive


### 4.5. Extractive + Abstractive

(1)
GB method: both extractive and abstractive text summarizations can be done using the GB method. This method uses a graph to rank the necessary sentences or terms and is an unsupervised learning technique. To extract the most pertinent sentences from a single text, the graphical technique is used. Based on global information that is iteratively collected from the entire network, GB ranking algorithms estimate the significance of a vertex in the graph. Text summarization is accomplished through the use of the GB method. The GB methods include the following submethods [28, 40](2)
LexRank: based on the idea of eigenvector centrality in a graph representation of sentences for NLP, it is a probabilistic GB technique for determining sentence significance. The adjacency matrix in a sentence graph representation is a connectivity matrix based on intrasentence cosine similarity(3)
Hyperlink-induced topic search (HITS): the authority and hub values are determined by a link analysis algorithm called hyperlink-induced topic search. The computation is only run on this set of results once the results of the search query are returned. An authority value is the sum of the scaled authority values of the pages it points to, and a hub value is the total of those scaled values(4)
PageRank: the PageRank algorithm ranks the search results by evaluating the importance or quality of inbound connections to a set of specific pages. Based on the significance of the page from which it comes, PageRank links have more weight(5)
TextRank: it is an unsupervised technique for automatically extracting a document’s most crucial keyword phrases. It calculates the degree of similarities between two sentences based on the content of each. This overlap is calculated by dividing the total number of common lexical tokens by the length of each phrase(6)
Positional power function: this ranking technique determines a vertex’s score as a function that takes into account both the quantity and quality of its ancestors(7)
Undirected graph: recursive GB ranking techniques can also be applied with undirected graphs, where a vertex’s out-degree is equal to its in-degree. For weakly linked networks with an equal number of edges and vertices, undirected graphs show slower convergence curves(8)
Weighted graphs: graphs constructed from natural language texts may contain several or insufficient links between the units (vertices) collected from the text. It could be advantageous to express and include the “strength” of the relationship between two vertices in the model since weight is applied to the matching edge that connects the two vertices. The edge weights are taken into account while determining the score assigned to a graph vertex. It is important to note that a similar technique can be used to integrate vertex weights(9)
GB attention mechanism: the importance score in the graph model is based on the relationships between all other phrases. The rank scores of the original sentences are computed using a combination of traditional attention and graph ranking algorithms in this mechanism, leading to a range of significance ratings for the actual phrases while decoding different states(10)
DL algorithm: information-driven ATS can become more effective, available, and user-friendly with the aid of DL models. Because they try to mimic how the human brain works, these models are quite promising for ATS. Because of how well its architecture fits with the complex structure of the language, deep neural networks are frequently used in NLP problems. For instance, each layer can tackle a specific task before transferring the output to the next. The DL algorithm includes the following subcomponents [28, 40]:
(a)
Graph convolutional network (GCN): it is a method for performing semisupervised learning on graph-structured data. It is based on an efficient CNN variant that operates directly on graphs. As a result, it integrates a label sampling model and GCN into an unsupervised learning framework to uncover underlying community structures by fusing topology and attribute information, inspired by GCN’s message pass mechanism and the local self-organizing property of community structure [44, 45](b)
RNN encoder-decoder: the RNN encoder-decoder design employs the sequence-to-sequence paradigm. The NN’s input sequence is transformed into an identical series of letters, words, or phrases using the sequence-to-sequence model [43, 46]. Examples of NLP applications include text summarization and machine translation. The difficulty with this RNN sequence-to-sequence is that it necessitates a large dataset. The training of datasets takes a long period. Because of this, the DL techniques described in the following subcomponents work better(c)
Long short-term memory (LSTM): input/read, memory/update, forget, and output gates make up the LSTM architecture’s repeating unit. The chain structure and an RNN have a lot in common. The initialization of the input gate’s vector is random. In later phases, the output of the prior step serves as the input for the current step. A single layer NN with a sigmoid activation function makes up the forget gate. Whether or not the information from the prior state should be remembered depends on the outcome of the sigmoid function. The memory gate regulates the impact of known information on unknowable information. The amount of fresh data sent to the following LSTM unit is managed by the output gate. The LSTM has the potential to generate a succinct abstractive summary(d)
Gated recurrent unit (GRU): it is a condensed version of an LSTM that just has two gates: an update gate and a reset gate with no explicit memory. The previously concealed state information is erased as soon as all reset gate components get close to zero. The candidate’s hidden state is only influenced by the input vector. In this case, the update gate functions as a forget gate. Although LSTM has a memory unit that provides more control, the GRU’s computation time is steadily getting slower(e)
Restricted Boltzmann machine (RBM): the NN with random probability distributions is known as a random probability distributed neural network (RBM). The network is composed of hidden layers of hidden neurons and a visible layer of visible neurons (input nodes) (hidden nodes). Each hidden node has a bidirectional connection to each input node. The bias node is linked to each concealed node. The input nodes are not linked in the visible layer(f)
NB classification: the most important keywords are taken out of the text using the NB classification approach. The NB strategy uses ML to extract the keyword from the input by calculating the differentiating keyword features in a text. The accuracy of summarization is increased by combining the NB, score, and timestamp ideas(g)
Query based (QB): the frequency counts of words or phrases used in QB text summarization are used to determine how frequently each sentence appears in a particular document. Search phrases containing sentences obtained higher scores than sentences that only contained a single search term. Focused on QB methods for text summarization, the sentences with the highest scores are then extracted, along with their structural contexts, for the output summary(h)
Generic summarization: the generic summaries highlight the key ideas in a document(i)
Q-network (QN): to approximate the ideal action-value function, which gauges the action’s long-term benefits for the agent, QN is utilized. The model may produce aQ value based on a candidate sentence and a partial summary (current state) (action). When the agent selects the candidate sentence to be a part of the summary, the output Q value shows the predicted value. In addition to these well-known models, several pretrained language models, like BERT, GPT-2, TransformerXL, and XLnet, have become more proficient at a variety of NLP tasks, including sentiment analysis, question answering, named entity recognition, textual similarity, and paraphrasing [47, 48]. These language models are customized with diverse task-specific goals after being pertained to enormous volumes of text data. These models are extremely beneficial because they have an unsupervised purpose of masked language modeling and next-sentence prediction. Pertained language models are often encoders for sentence and paragraph level classification tasks as well as problems with natural language comprehension. The following are some components of QN [40](j)
Bidirectional encoder representations from transformers (BERT): the BERT pretrained model is a straightforward and effective one. To pretrain deep bidirectional representations from the unlabeled text, BERT is designed to change both left and right contexts on all levels [49]. Additionally, BERT only has one output layer that offers cutting-edge models for a variety of tasks, including language inference and question-answering. During fine-tuning, it does not need any significant task-specific architecture changes. When learning the contextual relationships between words in a text, BERT uses a transformer mechanism. Additionally, it has two independent tools: a decoder that makes predictions and an encoder that interprets text input. The transformer encoder reads the complete sequence of words at once, in contrast to directional models that read the text input sequentially (left-to-right or right-to-left). As a result, it is thought to be bidirectional or, more precisely, nondirectional in some circumstances. This ability to effectively include both perspectives substantially helps BERT provide the best results(k)
GPT2: compared to the existing language models, the OpenAI GPT-2 has demonstrated a remarkable capacity to provide coherent and reliable summaries [50]. The GPT-2 is extremely similar to the decoder-only transformer and is not an original design. It is a large, transformer-based language model that was developed using the enormous Webtext dataset. The key distinction between GPT-2 and BERT is that the former uses transformer decoder blocks, while the latter employs transformer encoder blocks. Autoregressive models include the GPT2 and some more recent ones like TransformerXL and XLNet [51]. Every time a token is created and added to a sentence sequence, the notion behind autoregression is that the new line becomes the model’s following step’s input [40]. The detailed and extended classification of ATS algorithms has been depicted in Figure 4, and the limitation of these algorithms has been presented in Table 1Figure 4
An extended classification of ATS systems.Table 1
Limitations of ATS algorithms.
AlgorithmLimitationsUnsupervisedFuzzy logic(i) To improve the summarization quality, a redundancy elimination technique must be used in the postprocessing stage.Concept-based(ii) To reduce redundancy, which could impair the quality of the summary, similarity measurements must be used.Latent semantic(iii) It took a long time to create the LSA-generated summary.SupervisedMachine learning(i) For training and optimizing the sentence selection to create an effective summary, a huge quantity of data is required.Neural network(ii) In the training and application phases, it moves fairly slowly. Additionally needs human interference for training data.Conditional random fields(iii) Linguistic aspects are not taken into account in CRF. Additionally, a domain-specific external corpus is needed.Structure basedTree based(i) It fails to recognize the relationship between sentences as a result of ignoring the context and important phrases in the text. It continuously emphasizes syntax rather than semantics, which is another problem.Template based(ii) This strategy results in a lack of variability in the summaries because the templates are predefined.Rule-based(iii) The process of creating the regulations is time-consuming. The fact that the rules had to be manually drafted and presented is another difficulty.Ontology-based(iv) It takes a long time to prepare an appropriate ontology, and it cannot be applied to other domains.Multimodal semantic(v) The framework is currently examined manually by people; hence, an automatic evaluation is necessary.Semantic basedInformation item(i) Difficulty in constructing grammatical, coherent sentences from the text. Due to faulty parses, summaries also have very poor linguistic quality.Semantic graph(ii) This technique is only effective for abstract summarizing one document.Deep learning(iii) Building large amounts of training data manually requires human effort.Graph-based(iv) It disregards the significance of words and the issue with dangling anaphora.


### 4.6. Motivation and Application of ATS

This study attempts to expedite knowledge about ATS by providing an overview of all the research works that have been done for Ethiopian languages by using different approaches and/or algorithms. Additionally, it enables the development of new resources, databases, tools, and procedures to satisfy the demands of the commercial and research sectors. The development of NLPs made ATS useful for sentiment analysis and normal text document summaries. Additionally, ATS encourages a flexible approach to study in a variety of domains, including AI, NLP, cognitive science, and psychology. The ATS covers cutting-edge studies and potential future possibilities in this fascinating field using a variety of information sources. The application, which is discussed in the following section, is a crucial component of ATS research works. Applications for information retrieval, information extraction, question-answering, text mining, and analytics have recently been widely used by ATS. With a variety of applications, such as news summaries, email summaries, and domain-specific summaries, the ATS further enhances the search engine’s functionality. Here are some examples of ATS domain applications [28]:
(1)
Books or novel summarization (BNS): as brief texts are not suited for summarizing, ATS is typically used to summarize long documents like books, literature, or novels. Short writings are difficult to put into context, while large documents sometimes make for better summaries [40, 52](2)
Social posts or tweet summarization (SPOTS): on social networking sites like Facebook and Twitter, millions of messages and postings are created each day. The ATS can be used, sum up, as an important text in a useful way [40, 53](3)
Sentiment analysis (SA): SA is the study of people’s opinions, attitudes, and assessments of facts and circumstances. The SA uses fuzzy logic to categorize feelings and the majority of product review opinions as “positive” or “negative.” For example, market analysts may summarize the opinions of hundreds of people using ATS, which is quite beneficial [40, 54](4)
News summarization (NS): the ATS assists in providing news summaries from numerous websites, including CNN and other well-known news portals. The headline of the piece is sometimes the key focal point that ATS draws from the newspaper story [40, 55](5)
Email summarization (ES): email conversations are typically not syntactically well-formed domains and are unstructured, making them difficult to summarize. The ATS often uses linguistic techniques and ML algorithms to extract noun phrases from email communications and produce a summary of them [40, 56, 57](6)
Legal document summarization (LDS): to describe a judicial judgment document, ATS looks up pertinent precedent using legal questions and rhetorical techniques. Keywords, important phrase matching, and case-based analysis are just a few techniques used in a hybrid approach [40, 58](7)
Biomedical document summarization (BDS): the ATS uses a GB summarization approach in conjunction with genetic clustering and connectivity data. While connection statistics show the relative importance of the study, genetic clustering reveals the many themes in a biological document [40, 41, 59](8)
Summarization of scientific papers (SSP): scientific texts are well-organized texts that include the opinions of many researchers on a single topic. Additionally, rather than being available in general texts, the key aspects of a scientific document are typically indicated in tables and figures. A technological survey of scientific documents is created using a multiple document ATS framework, which incorporates two approaches. To determine the content of the original and related referenced research works, first, follow and collect the citations and then utilize summarizing techniques [40, 60]


## 4.1. Unsupervised Learning Methods

Unsupervised extractive text summarization, which is more like standard ATS, selects key sentences from documents. This algorithm chooses sentences without using labeled summaries during the training of either a text or dataset. Those techniques are also efficient because they do not need user input or human overviews to identify the document’s key components. Furthermore, unsupervised methods are effective because they do not require user feedback or human overviews to determine the document’s essential features. Unsupervised text summarization is more efficient than supervised text summarization and is better suited to long text summaries. The following are some unsupervised techniques or methods for summarizing texts or documents:(1)
Fuzzy logic (FL): the choice of fuzzy rules and membership functions is often part of the design process for a fuzzy logic method. A fuzzifier, inference engine, defuzzifier, and knowledge base are its four constituent parts. The most crucial passage from the source text or context is also chosen using a fuzzy logic method. However, for the fuzzy logic method to produce better outcomes, redundancy elimination must be used(2)
Concept-based method: the concept-based method takes concepts from the source content and reduces repetition by using similarity measurements. These retrieved concepts are calculated, and the sentences are scored based on importance. The constraints of both this method and fuzzy logic are the same, but fuzzy logic is superior since it can handle ambiguous circumstances better(3)
Latent semantic analysis (LSA) method: an algebraic statistical method called LSA can be used to uncover sentences’ hidden semantic components. The LSA is an unsupervised learning method that extracts knowledge and related terms from input texts. The benefit of this strategy is that it may be used without any external training or template to identify comparable terms that appear in different phrases. However, the LSA approach has some limitations, such as not examining word order, syntactic relations, or morphologies. Furthermore, it does not rely on outside information or sources other than the information found in the input documentFinally, this method is not comparable due to flaws such as performance degradation when using inhomogeneous datasets. Additionally, the semantic summary of texts benefits greatly from the use of this strategy.


## 4.2. Supervised Learning Methods

Sentence-level classifiers that learn to differentiate between summarized and nonsummarized sentences are known as supervised learning methods. Summaries written by humans and collections of documents can both learn the traits of the sentences they contain. In addition, it has substantial limitations in producing context summaries manually and requires more labeled training samples for classification [28, 40].
(1)
ML method: the ML method is used to classify the sentences as summary or nonsummary classes using training data. When extractive summaries are needed for numerous document copies, several techniques are used. Notably, each document’s sentence is depicted in this case as vectors. The ML methods are mostly applied to a set of training datasets that contain trainable texts. In the training phase, a variety of training manuals are fed into input documents and categorized according to the sentence weight. Although a straightforward regression model frequently outperforms classifiers, it still needs a large amount of training data whether an NN model has multiple layers that depend only on the data which is the only layer in the Ml models. Because of this, NN models are improving in usability and user appeal on ATS. Other common preprocessing information retrieval procedures, like stop-word removal, case folding, and stemming, are also included in the ML methods(2)
NN method: a three-layered feedforward network is used in a NN approach to learning the features of words during model training. The phase of feature matching is important, and some phases identify the relationship between the qualities. The steps to define meaningful sentences are to eliminate infrequent features, combine frequent elements, and then rank the phrases. As the network learns from its training data, the NN approach is also utilized to train according to the human’s preferences or needs. As a more complex variant of ML, NN algorithms outperform ML algorithms when there are more layers and hidden layers added. Hence, the RankNet approach, which was created in, likewise uses neural nets to automatically classify the pertinent sentences in the text. It uses a two-layer backpropagation NN that was trained using the RankNet technique(3)
Conditional random field (CRF) method: the CRFs are ML-based statistical modeling techniques that offer a consistent prediction. To extract the appropriate features, CRF employs nonnegative matrix factorization (NMF) techniques. The document’s introduction sentence is then defined using the appropriate components. By providing a more accurate representation of sentences and sections, CRF’s key advantage is distinguishing appropriate features. This technique has a major flaw in that it is domain-specific, necessitating the use of an external domain-specific framework throughout the training phase. Without initially establishing a time-consuming domain foundation, this methodology can be universally applied to any text. As a result, algorithms based on ML and NN are still preferable. These techniques rely on optimization, statistical analysis, topic selection, sentence centrality, or clustering. Because the optimization method requires a lot of processing and is expensive, researchers used a genetic algorithm to determine the best weights. It is necessary to specify the necessary number of iterationsTo generate abstractive summaries, language generation and compression strategies are required. Abstractive text summarization is also divided into two categories to produce better abstractive summaries: structured and semantic [40].


## 4.3. Structure-Based Methods

The original document calls for newly created sentences to summarize in the abstractive summary. The structure-based method interprets phrases from source materials according to a predetermined framework without distorting their original meaning. Structure-based methods, such as templates, tree-based, ontology-based, and rule-based structures, mostly rely on predefined forms and spatial reasoning schemas [28, 40].
(1)
Tree-based method: to produce an abstractive summary, the tree-based method first identifies sentences that share common knowledge and facts before combining them. Tree linearization, which derives from several dependency trees, is the name of this structure that resembles a tree. Dependency trees serve as a visual representation of a document’s source text. A syntactic tree is used by the tree-based approach to analyze various documents and locate common information. These techniques likewise result in fewer repetitive summaries, but they are unable to identify sentence relationships in isolation from context. As a result, it ignores important textual sentences. The method’s constant emphasis on syntax rather than semantics is another drawback. This approach stands out in structure-based methodologies despite these problems because of its fluency in summarizing(2)
Template-based method: by identifying similarities with a template space, the topic or content is extracted into possible phrases and speakers in the template-based methods. When a document needs a predetermined guideline or a custom template for the summary, the template-based method is employed. This process creates detailed summaries that are coherent and depending on the option; the content’s speakers and phrases are chosen. The problem with this strategy is that there is not much variability in the summaries because the templates for summaries are always predefined. Consequently, in contrast to the tree-based technique, it is unable to provide fluent summaries(3)
Rule-based method: through investigating, the rule-based technique uncovers information and assessments of key ideas in source documents. For example, what are the topics under interrogation and questioning? Answering these questions seeks to generate an abstractive summary, such as “What is the time-being of the story or topic?” and “What are the names of the people on the table, according to certain rules?”. When input documents need to be represented as classes and lists of aspects, rule-based approaches are employed instead of query-based ones. The creation of the regulations requires the use of this procedure, which takes time. This method is less effective than the other ways stated before in this paragraph due to the manually written rules(4)
Ontology-based method: a knowledge-based strategy called ontology serves as a formal name and characterization of the many entity kinds inside a given domain. This technique uses a foundation of information to enhance the effectiveness of summarization. When a document has a knowledge structure or is regularly built around the same topic, ontology-based methods excel. To provide coherent summaries, this strategy concentrates on documents that are specifically connected to a given area. This approach takes time, just like the rule-based approach does


## 4.4. Semantic-Based Methods

With a strong emphasis on noun and verb phrase identification, semantic-based approaches translate the linguistics of a document’s contents into a natural language generation (NLG) system. Less redundant and grammatically correct sentences can be created using these techniques. These methods have the drawback of occasionally ignoring important information or data, even though it is grammatically correct. This method includes the following [40]:
(1)
Multimodal semantic method: to comprehend both visual and word concepts from a document, the multimodal-based method is applied. To communicate text and images/pictures in multimodal materials, the multimodal semantic model collects ideas and creates relationships between them. Knowledge representation based on objects is the basis of a semantic model based on nodes and connections. Nodes represent concepts, whereas connections show the links between concepts. The information density measure is used to assess an expression’s completeness, relationships with other expressions, and repeats. Finally, ideas that summarize the selected concepts are created(2)
Semantic GB method: the semantic GB approach condenses a document by creating a rich semantic graph (RSG) for the original document and then condensing the resulting semantic graph. This method’s strength is its ability to produce succinct, coherent, and grammatically sound sentences using smaller networks. By giving weights to the nodes and edges of phrases, the semantic-graph-based model primarily extracts semantic information. Because of this, this approach generally performs well, but it does necessitate a semantic representation of the text(3)
Information item method: instead of creating an abstract from the words in the text file, the information item-based method summarizes a text file based on its abstract. In this method, a summary is created using an abstract representation of the source information. An information item is a minor part of a source document. The approach then extracts information from a text that should have a logical flow of information. An approach that is based on information items yields summaries that are shorter and less repetitive


## 4.5. Extractive + Abstractive

(1)
GB method: both extractive and abstractive text summarizations can be done using the GB method. This method uses a graph to rank the necessary sentences or terms and is an unsupervised learning technique. To extract the most pertinent sentences from a single text, the graphical technique is used. Based on global information that is iteratively collected from the entire network, GB ranking algorithms estimate the significance of a vertex in the graph. Text summarization is accomplished through the use of the GB method. The GB methods include the following submethods [28, 40](2)
LexRank: based on the idea of eigenvector centrality in a graph representation of sentences for NLP, it is a probabilistic GB technique for determining sentence significance. The adjacency matrix in a sentence graph representation is a connectivity matrix based on intrasentence cosine similarity(3)
Hyperlink-induced topic search (HITS): the authority and hub values are determined by a link analysis algorithm called hyperlink-induced topic search. The computation is only run on this set of results once the results of the search query are returned. An authority value is the sum of the scaled authority values of the pages it points to, and a hub value is the total of those scaled values(4)
PageRank: the PageRank algorithm ranks the search results by evaluating the importance or quality of inbound connections to a set of specific pages. Based on the significance of the page from which it comes, PageRank links have more weight(5)
TextRank: it is an unsupervised technique for automatically extracting a document’s most crucial keyword phrases. It calculates the degree of similarities between two sentences based on the content of each. This overlap is calculated by dividing the total number of common lexical tokens by the length of each phrase(6)
Positional power function: this ranking technique determines a vertex’s score as a function that takes into account both the quantity and quality of its ancestors(7)
Undirected graph: recursive GB ranking techniques can also be applied with undirected graphs, where a vertex’s out-degree is equal to its in-degree. For weakly linked networks with an equal number of edges and vertices, undirected graphs show slower convergence curves(8)
Weighted graphs: graphs constructed from natural language texts may contain several or insufficient links between the units (vertices) collected from the text. It could be advantageous to express and include the “strength” of the relationship between two vertices in the model since weight is applied to the matching edge that connects the two vertices. The edge weights are taken into account while determining the score assigned to a graph vertex. It is important to note that a similar technique can be used to integrate vertex weights(9)
GB attention mechanism: the importance score in the graph model is based on the relationships between all other phrases. The rank scores of the original sentences are computed using a combination of traditional attention and graph ranking algorithms in this mechanism, leading to a range of significance ratings for the actual phrases while decoding different states(10)
DL algorithm: information-driven ATS can become more effective, available, and user-friendly with the aid of DL models. Because they try to mimic how the human brain works, these models are quite promising for ATS. Because of how well its architecture fits with the complex structure of the language, deep neural networks are frequently used in NLP problems. For instance, each layer can tackle a specific task before transferring the output to the next. The DL algorithm includes the following subcomponents [28, 40]:
(a)
Graph convolutional network (GCN): it is a method for performing semisupervised learning on graph-structured data. It is based on an efficient CNN variant that operates directly on graphs. As a result, it integrates a label sampling model and GCN into an unsupervised learning framework to uncover underlying community structures by fusing topology and attribute information, inspired by GCN’s message pass mechanism and the local self-organizing property of community structure [44, 45](b)
RNN encoder-decoder: the RNN encoder-decoder design employs the sequence-to-sequence paradigm. The NN’s input sequence is transformed into an identical series of letters, words, or phrases using the sequence-to-sequence model [43, 46]. Examples of NLP applications include text summarization and machine translation. The difficulty with this RNN sequence-to-sequence is that it necessitates a large dataset. The training of datasets takes a long period. Because of this, the DL techniques described in the following subcomponents work better(c)
Long short-term memory (LSTM): input/read, memory/update, forget, and output gates make up the LSTM architecture’s repeating unit. The chain structure and an RNN have a lot in common. The initialization of the input gate’s vector is random. In later phases, the output of the prior step serves as the input for the current step. A single layer NN with a sigmoid activation function makes up the forget gate. Whether or not the information from the prior state should be remembered depends on the outcome of the sigmoid function. The memory gate regulates the impact of known information on unknowable information. The amount of fresh data sent to the following LSTM unit is managed by the output gate. The LSTM has the potential to generate a succinct abstractive summary(d)
Gated recurrent unit (GRU): it is a condensed version of an LSTM that just has two gates: an update gate and a reset gate with no explicit memory. The previously concealed state information is erased as soon as all reset gate components get close to zero. The candidate’s hidden state is only influenced by the input vector. In this case, the update gate functions as a forget gate. Although LSTM has a memory unit that provides more control, the GRU’s computation time is steadily getting slower(e)
Restricted Boltzmann machine (RBM): the NN with random probability distributions is known as a random probability distributed neural network (RBM). The network is composed of hidden layers of hidden neurons and a visible layer of visible neurons (input nodes) (hidden nodes). Each hidden node has a bidirectional connection to each input node. The bias node is linked to each concealed node. The input nodes are not linked in the visible layer(f)
NB classification: the most important keywords are taken out of the text using the NB classification approach. The NB strategy uses ML to extract the keyword from the input by calculating the differentiating keyword features in a text. The accuracy of summarization is increased by combining the NB, score, and timestamp ideas(g)
Query based (QB): the frequency counts of words or phrases used in QB text summarization are used to determine how frequently each sentence appears in a particular document. Search phrases containing sentences obtained higher scores than sentences that only contained a single search term. Focused on QB methods for text summarization, the sentences with the highest scores are then extracted, along with their structural contexts, for the output summary(h)
Generic summarization: the generic summaries highlight the key ideas in a document(i)
Q-network (QN): to approximate the ideal action-value function, which gauges the action’s long-term benefits for the agent, QN is utilized. The model may produce aQ value based on a candidate sentence and a partial summary (current state) (action). When the agent selects the candidate sentence to be a part of the summary, the output Q value shows the predicted value. In addition to these well-known models, several pretrained language models, like BERT, GPT-2, TransformerXL, and XLnet, have become more proficient at a variety of NLP tasks, including sentiment analysis, question answering, named entity recognition, textual similarity, and paraphrasing [47, 48]. These language models are customized with diverse task-specific goals after being pertained to enormous volumes of text data. These models are extremely beneficial because they have an unsupervised purpose of masked language modeling and next-sentence prediction. Pertained language models are often encoders for sentence and paragraph level classification tasks as well as problems with natural language comprehension. The following are some components of QN [40](j)
Bidirectional encoder representations from transformers (BERT): the BERT pretrained model is a straightforward and effective one. To pretrain deep bidirectional representations from the unlabeled text, BERT is designed to change both left and right contexts on all levels [49]. Additionally, BERT only has one output layer that offers cutting-edge models for a variety of tasks, including language inference and question-answering. During fine-tuning, it does not need any significant task-specific architecture changes. When learning the contextual relationships between words in a text, BERT uses a transformer mechanism. Additionally, it has two independent tools: a decoder that makes predictions and an encoder that interprets text input. The transformer encoder reads the complete sequence of words at once, in contrast to directional models that read the text input sequentially (left-to-right or right-to-left). As a result, it is thought to be bidirectional or, more precisely, nondirectional in some circumstances. This ability to effectively include both perspectives substantially helps BERT provide the best results(k)
GPT2: compared to the existing language models, the OpenAI GPT-2 has demonstrated a remarkable capacity to provide coherent and reliable summaries [50]. The GPT-2 is extremely similar to the decoder-only transformer and is not an original design. It is a large, transformer-based language model that was developed using the enormous Webtext dataset. The key distinction between GPT-2 and BERT is that the former uses transformer decoder blocks, while the latter employs transformer encoder blocks. Autoregressive models include the GPT2 and some more recent ones like TransformerXL and XLNet [51]. Every time a token is created and added to a sentence sequence, the notion behind autoregression is that the new line becomes the model’s following step’s input [40]. The detailed and extended classification of ATS algorithms has been depicted in Figure 4, and the limitation of these algorithms has been presented in Table 1Figure 4
An extended classification of ATS systems.Table 1
Limitations of ATS algorithms.
AlgorithmLimitationsUnsupervisedFuzzy logic(i) To improve the summarization quality, a redundancy elimination technique must be used in the postprocessing stage.Concept-based(ii) To reduce redundancy, which could impair the quality of the summary, similarity measurements must be used.Latent semantic(iii) It took a long time to create the LSA-generated summary.SupervisedMachine learning(i) For training and optimizing the sentence selection to create an effective summary, a huge quantity of data is required.Neural network(ii) In the training and application phases, it moves fairly slowly. Additionally needs human interference for training data.Conditional random fields(iii) Linguistic aspects are not taken into account in CRF. Additionally, a domain-specific external corpus is needed.Structure basedTree based(i) It fails to recognize the relationship between sentences as a result of ignoring the context and important phrases in the text. It continuously emphasizes syntax rather than semantics, which is another problem.Template based(ii) This strategy results in a lack of variability in the summaries because the templates are predefined.Rule-based(iii) The process of creating the regulations is time-consuming. The fact that the rules had to be manually drafted and presented is another difficulty.Ontology-based(iv) It takes a long time to prepare an appropriate ontology, and it cannot be applied to other domains.Multimodal semantic(v) The framework is currently examined manually by people; hence, an automatic evaluation is necessary.Semantic basedInformation item(i) Difficulty in constructing grammatical, coherent sentences from the text. Due to faulty parses, summaries also have very poor linguistic quality.Semantic graph(ii) This technique is only effective for abstract summarizing one document.Deep learning(iii) Building large amounts of training data manually requires human effort.Graph-based(iv) It disregards the significance of words and the issue with dangling anaphora.


## 4.6. Motivation and Application of ATS

This study attempts to expedite knowledge about ATS by providing an overview of all the research works that have been done for Ethiopian languages by using different approaches and/or algorithms. Additionally, it enables the development of new resources, databases, tools, and procedures to satisfy the demands of the commercial and research sectors. The development of NLPs made ATS useful for sentiment analysis and normal text document summaries. Additionally, ATS encourages a flexible approach to study in a variety of domains, including AI, NLP, cognitive science, and psychology. The ATS covers cutting-edge studies and potential future possibilities in this fascinating field using a variety of information sources. The application, which is discussed in the following section, is a crucial component of ATS research works. Applications for information retrieval, information extraction, question-answering, text mining, and analytics have recently been widely used by ATS. With a variety of applications, such as news summaries, email summaries, and domain-specific summaries, the ATS further enhances the search engine’s functionality. Here are some examples of ATS domain applications [28]:
(1)
Books or novel summarization (BNS): as brief texts are not suited for summarizing, ATS is typically used to summarize long documents like books, literature, or novels. Short writings are difficult to put into context, while large documents sometimes make for better summaries [40, 52](2)
Social posts or tweet summarization (SPOTS): on social networking sites like Facebook and Twitter, millions of messages and postings are created each day. The ATS can be used, sum up, as an important text in a useful way [40, 53](3)
Sentiment analysis (SA): SA is the study of people’s opinions, attitudes, and assessments of facts and circumstances. The SA uses fuzzy logic to categorize feelings and the majority of product review opinions as “positive” or “negative.” For example, market analysts may summarize the opinions of hundreds of people using ATS, which is quite beneficial [40, 54](4)
News summarization (NS): the ATS assists in providing news summaries from numerous websites, including CNN and other well-known news portals. The headline of the piece is sometimes the key focal point that ATS draws from the newspaper story [40, 55](5)
Email summarization (ES): email conversations are typically not syntactically well-formed domains and are unstructured, making them difficult to summarize. The ATS often uses linguistic techniques and ML algorithms to extract noun phrases from email communications and produce a summary of them [40, 56, 57](6)
Legal document summarization (LDS): to describe a judicial judgment document, ATS looks up pertinent precedent using legal questions and rhetorical techniques. Keywords, important phrase matching, and case-based analysis are just a few techniques used in a hybrid approach [40, 58](7)
Biomedical document summarization (BDS): the ATS uses a GB summarization approach in conjunction with genetic clustering and connectivity data. While connection statistics show the relative importance of the study, genetic clustering reveals the many themes in a biological document [40, 41, 59](8)
Summarization of scientific papers (SSP): scientific texts are well-organized texts that include the opinions of many researchers on a single topic. Additionally, rather than being available in general texts, the key aspects of a scientific document are typically indicated in tables and figures. A technological survey of scientific documents is created using a multiple document ATS framework, which incorporates two approaches. To determine the content of the original and related referenced research works, first, follow and collect the citations and then utilize summarizing techniques [40, 60]


## 5. Related Works

The first proposed idea of ATS was in 1958, in the sense of figuring out how words are distributed inside sentences and identifying the document’s keywords [61]. Since then, numerous summarizing techniques have been created using various methodologies and for different objectives.According to [2], deep neural sequence-to-sequence models, reinforcement learning (RL) approaches, and transfer learning (TL) approaches, including pretrained language models, have been identified as cutting-edge technologies that demonstrate cutting-edge performance and accuracy in abstractive ATS (PTLMs). The GB transformer architecture and PTLMs have had a significant impact on NLP applications. Furthermore, the incorporation of recent mechanisms, such as the knowledge-enhanced mechanism, improved the results significantly. This study provides a thorough examination of recent research advances in the field of abstractive text summarization for works published in the last six years. Past and present issues are described, as are proposed solutions. Furthermore, abstractive ATS datasets and evaluation metrics are highlighted. Finally, the paper compares the best models and discusses future research directions.According to [3], ATS is in high demand to address the ever-growing volume of text data available online to find relevant information more quickly. In this study, the ATS methodology was proposed for the Hindi language using the real coded genetic algorithm (RCGA) over the Kaggle dataset’s health corpus. The methodology was divided into five phases: preprocessing, feature extraction, processing, sentence ranking, and summary generation. Extensive testing on various feature sets was carried out, in which distinguishing features, namely, sentence similarity and named entity features, are combined with others to compute the evaluation metrics. The experiments have been carried out on various combinations of eight features. They have been distinguished by sentence similarity and named entity features, which were combined with others to compute the evaluation metrics. The top 14 feature combinations are analyzed at three different extraction rates: 25%, 50%, and 65%. The researchers have scored precision of 79%, recall of 78%, and 78% f-score for 25% extraction rate and precision of 83%, recall of 84%, and 83% f-score for 50% extraction rate, and finally, precision of 83%, recall of 91%, and 87% of f-score for 65% extraction rate, respectively. The system produces a summary in about 55 seconds, 108 seconds, and 119 seconds for extraction rates of 25%, 50%, and 65%, respectively. The system summary’s average performance for Rouge-1 was 79% and for Rouge-2 was 66%, which was comparable to the reference summary. When compared to other available tools, the overall summary yields promising results with a reasonable compression rate and efficiency.The work [62] has employed a surface-level technique that relies on features, cue phrase, title words, header (first sentence of the document), words in the header, the first sentence in the paragraph, and extremely common terms, which are statistical, to produce summaries. The absence of a stemmer, which alters the frequency of words in a text, was made by the authors. However, their summarization system’s performance was enhanced by using a comprehensive list of common words. The work [63] has made an effort to create a text summarizing system that produces excerpts from a single court decision. Three steps were used in their task. First, the text or legal judgment was divided into five sections according to five preestablished themes: introduction, justification, fact, judicial analysis, and conclusion. The significance of sentences was then assessed using statistical indicators such as cue phrases and sentence position. The next step was to construct metasummaries for each theme with a 20% extraction rate, which were then concatenated to make a single summary. In the work of [64], it has been built to summarize news articles in the Amharic language that has been shared on Facebook and Twitter. The clustered group of protest posters in experiment one had the greatest f score of 87.07% for extraction rate at 30%. The greatest f-score in the second trial was 84% for extraction rate at 30% in drought post groups. The greatest f-score was 91.37% for the extraction rate at 30% in the third experiment’s sports post groups, and it is 93.52% in the fourth experiment’s summary post text extraction rate at 30%.The work of [65] was modifying the open text summarizer (OTS) open-source program to produce and evaluate extracts from Amharic language news texts. The relative value of a phrase in a text is determined by their work using the frequency of terms. They used two experimental setups because their work focuses on evaluating how well the tool performs: experiment 1, which employs the original Porter stemmer, and experiment 2, which uses a stemmer that was adapted from the work of [66]. The performance of OTS in summarizing Amharic language text was encouraging after a series of tests on each arrangement, with experiment 2 outperforming experiment 1. Authors of [67] have built an algorithm that can summarize a document by their performance both objectively and subjectively in Afan Oromo language. The performance of the summarizers was tested based on subjective as well as objective evaluation methodologies. In this study, weights were assigned to the sentences that would be retrieved for the summary using term frequency and sentence position algorithms with language-specific lexicons.The work [68] has looked into how to extract information from Amharic language news articles using the Naive Bayes (NB) classifier. Training and testing were the two steps used in this research. They have considered elements including title words, cue words, sentence positioning, and the inclusion of thematic words to gauge the likelihood that a given sentence would appear in a summary. Even though their method was domain-specific and required a sizable training corpus, the results indicated that it was a promising one.The work [69] has proposed a latent semantic analysis (LSA) for automatic Amharic language text summarization. TopicLSA and LSAGraph were the two methodologies that were combined in their work. Each text was represented as a graph with terms acting as nodes and edges representing the semantic connections between phrases. To compute the relevance score of words, the GB techniques were run onto the graph iteratively till they converge. Additionally, sentences that did not directly connect to the issue were punished and excluded from the summary.Statistical methods fail to capture the key topics of a document because their techniques are unaware of the relationships between textual units in the document [62, 63, 65]. As a result, key sentences that reflect the document’s essential notions may be omitted from the summary. The [70]’s work was domain-specific, was difficult to adapt to any language, necessitates extensive training corpora, and is hence very resource-intensive. The [69]’s technique becomes ineffective when the body of text to be summarized grows since it rejects sentences that, despite being appropriate for a summary, do not earn latent space or dimension. Afan Oromo language automatic news text summarizer based on sentence selection function has been created [71]. This work aims on constructing an efficient extractive Afan Oromo language automatic news text summarizer, using systematic integration of features like sentence position, keyword frequency, cue phrase, sentence length handler, the incidence of numbers, and events like time, date, and month in sentences. Abbreviations, synonyms, stop words, suffixes, numbers, and names (time, date, and month) gathered from both secondary and primary sources were among the data that help the system’s development. Seven experimental scenarios have been used to test the system, and both subjective and objective judgments have been produced. The subjective evaluation focuses on evaluation of the structure of the summary like referential integrity and nonredundancy, coherence, and informativeness of the summary. The objective evaluation employs criteria like precision, recall, and f-score. Subjective evaluation yields the following results: 68% coherence, 75% referential integrity, and 88% informativeness. Different methods and tests have been used as a result of the added features, producing an f-score of 87.47%. In the QB automatic summarizer for Afan Oromo language text [72], 40 news articles from the corpus were used. The best summary was created manually by three language specialists from the language division of the Oromia Culture and Tourism Bureau. The intrinsic evaluation technique was utilized for evolution purposes. Both objective and subjective evaluations were involved. The subjective evaluation assesses the linguistic quality, such as informativeness and coherence, using the scores on five scale measures by human evaluators. The objective evaluation assesses the performance of the system using standard information retrieval (IR) evaluation metrics (precision, recall, and f-score). The results of the assessments showed that the proposed system reported an f-score of 82%, 78%, and 82% at summary extraction rates of 10%, 20%, and 30%, respectively, and when the vector space model (VSM) was employed combined with position algorithms. Additionally, when both approaches were employed together, the suggested system’s informativeness and coherence both recorded their best performance summaries of 59%, 77%, and 91% average scores on five scale measures. An Afan Oromo language news text summary has been developed by using the sentence scoring method (SSM) [73]. Nine experimental circumstances were used to evaluate the proposed method both subjectively and objectively. The summary’s structure was subjectively evaluated for referential clarity, informativeness, grammatical correctness, and coherence. They also looked for any redundancies. The performance of the proposed system at 20%, 30%, and 40% extraction rate grammatical correctness was 90%, 90%, and 92%, respectively, concerning redundancy at 20%, 30%, and 40% extraction rate performance of the summarizer system was 72%, 82%, and 84%, respectively, and 20%, 30%, and 40% extraction rate performs 66%, 74%, and 86% in concerning referential clarity. Coherency of the summary evaluation performed at 20%, 30%, and 40% extraction rate 62%, 66%, and 72%, respectively. The performances of the system regarding the informativeness at 20%, 30%, and 40% extraction rates was 74%, 78%, and 86% respectivelly. The recall, precision, and f-score were the three criteria used in the system’s objective evaluations, which it completed with a score of 86.1%. The work [74] uses morphological analysis, and integrated development of a QB document summarization for Afan Oromo language has been developed. Due to its concerns of thoughtful, intellectual, and creative behavior throughout problem-solving and the generation of knowledge, this work adheres to the design science analysis technique. The developed QB framework has used the term weight methodology of TF-IDF. For morphological analysis, development tools like HornMorpho were used, whereas NLP toolkits were used for text processing. The extraction rate of 10%, 20%, and 30% had all been tested by the system. For objective evaluation, the results were assessed using recall, precision, and f-score; in contrast, language consultants assessed subjective analysis. In light of this, the evaluation revealed that the suggested method achieved f-scores of 90%, 91%, and 93% at summary extraction rates of 10%, 20%, and 30%, respectively. When both approaches were combined, the proposed system’s informativeness and coherence also showed their best performance summary, with average scores of 51.67%, 56.67%, and 54.17% on five scale measures. This was achieved at extraction rates of 10%, 20%, and 30%, respectively. In [75], the Afan Oromo language news text summarizer has been developed. The performance of the summarizer was tested based on subjective as well as objective evaluation methodologies. The findings of the objective evaluation show that the three summarizers, method 1, method 2, and method 3, registered f-score values of 34%, 47%, and 81%. Their tests showed that method 3 performed 47% and 34% better than the two summarizers (method 1 and method 2). Furthermore, according to the subjective evaluation results, the three summarizers’ (method 1, method 2, and method 3) performances in terms of informativeness, linguistic quality, coherence, and structure are 34.37%, 37%, and 62.5%; 59.37%, 60%, and 65%; and 21.87%, 28.12%, and 75% for methods 1, 2, and 3, respectively. The outcomes were consistent across subjective and objective evaluations. Method 3 of the summarizer, which combines the word frequency and enhanced position approaches, performs better than previous summarizers, with method 2 coming in second. Afan Oromo language text summary using word embedding has been developed by [76]. Three experimental scenarios have been used to evaluate the proposed system, and both subjective and objective judgments have been made. The subjective evaluation was focused on evaluating the summary’s structure, including its informativeness, coherence, referential clarity, lack of repetition, and grammar. They have employed metrics including precision, recall, and f-score in the objective evaluation. The outcome of subjective evaluation is 83.33% informativeness, 78.8% referential integrity and grammar, and 76.66% structure and coherence. This work also achieved 52.70% of precision, 42.20% of recall, and 46.80% f-score by using the acquired data. However, when compared to the prior works using the identical data used in their work, the summarizer’s overall performance outperformed by 64.80% of precision, 62.60% of recall, and 5.80% f-score. The Tigrinya language ATS has been developed by [77]. They have employed 30 news pieces for experimentation, which were gathered from the websites of the Aiga forum and Dimtsi Weyane Tigray. The summaries produced by the system were then compared to manual summaries created by human reviewers as part of the summarizing system’s evaluation. According to the experiments, the system recorded 46%, 46%, and 46% for recall, precision, and f-score for the feature of phrase frequency, respectively. In the case of title word, the registered recall, precision, and f-score values were 46%, 50%, and 48%, respectively. The experimental findings demonstrate how the suggested approach improves the summarizer. Overall, according to the experiment’s findings, title words outperformed term frequencies in both subjective and objective evaluations. Another work [78] has developed WordNet-based topic-based Tigrigna text summarization. Since news reports typically place crucial information at the beginning of the document rather than the end, the researchers conducted an experiment with news articles and included the first sentence of the document in the summary. The proposed approach yields the best result, 50.14% precision/recall of the original text, according to their evaluation of it at a 25% extraction rate summary. Additionally, they have compared their system to other summary techniques created for other languages, and their method greatly raises the quality of the summation. Authors of [79] have created OTS-based automated Amharic language multiple document news text synthesis. The Ethiopian news reporters, Addis Admass, Addis Zena, and the Walta Information Center served as the study’s data sources (WIC). Thirty tests were carried out utilizing 20%, 30%, and 40% extraction rates on a total of 35 Amharic language single text news. The single text news was then divided into ten multiple document text news. In the work, sentence extraction-based summarization for news articles written in Amharic for several documents was introduced. The study looked at the optimal extraction rate for text summary of numerous documents in Amharic language. Additionally, they demonstrated how the OTS can be modified and utilized to summarize news stories written in several Amharic language documents. The Amharic language rules that were employed for text summarization and their influence on the summarizer were also explored. The performance of the modified OTS was tested using intrinsic evaluation methodologies. Both subjective and objective evaluation strategies were drawn from the intrinsic evaluation techniques. According to a subjective evaluation method, the performance of the summary generated at a 20% extraction rate was 60.00%, followed by scores of 76.00% for the summary created at a 30% extraction rate and 82.00% for the final summary created at a 40% extraction rate. The summarizer performed an average of 52.14% f-score at 20% extraction rate, 56.31% f-score at 30% extraction rate, and 57.58% f-score at 40% extraction rate, according to the objective evaluation approach. In general, the objective and subjective evaluation metrics from the summarizer have yielded positive results for the researchers. Since the proposed method, an extractive-based text summarizer, lacks coherence, other advanced systems, such as abstractive-based summarizers, adding extra statistical features, using complete corpora, and trying various methods like anaphora resolution, lexical chains, and discourse structure, have been recommended.According to [80], as a research topic, the researchers have chosen the Afan Oromo text summarization in DL. The study’s primary goal was to design a system and implement extractive and abstractive Afan Oromo proclamation text summarization to develop effective and efficient summarization types, as well as to assess the extent of the algorithms’ fitness. Here, 583 articles from 27 Afan Oromo proclamations were used as input data. As a result, for text summarization of the dataset, abstractive text summarization models (sequence-to-sequence decoder with attention) and extractive text summarization models (TextRank) were developed. To evaluate the text summaries produced, various comparison measures (Rouge-1 and Rouge-2 percentage, count vectorizer, TF-IDF vectorizer, and soft-cosine similarity) were used. In this case, the results of the Rouge-1 and Rouge-2 measurement percentage indexes were higher for abstractive summarization than for extractive summarization. According to the researchers’ point of view, aside from the algorithms and models used in both summarization methods, they are appropriate for Afan Oromo proclamation text summarizations.According to [81], ATS can be done in two ways: extractive and abstractive, or both at the same time. They focused on abstractive summarization in this study because it produces more human-like summarization results. They developed a Turkish news summarization benchmark dataset for the study by crawling the news title, short news, news content, and keywords from various news agency web portals over the last 5 years. The dataset has been made freely available to researchers. The DL network was trained using the prepared dataset’s news headlines and short news contents, and the network was expected to create the news headline as the short news summary. The precision, sensitivity, and f-scores of Rouge-1, Rouge-2, and Rouge-L were used to compare the performance of this study. The study’s performance values were presented for each sentence as well as by averaging the results from 50 randomly selected sentences. Rouge-1, Rouge-2, and Rouge-L have f-scores of 43.17%, 21.94%, and 43.34%, respectively. The approach’s performance results indicate that it is promising for Turkish text summarization studies, and the prepared dataset has added values to the study.According to [82], the goal of ATS is to extract the essence of the original text and generate a concise and readable summary automatically. Recently, DL-based abstractive ATS models have been developed to better balance and develop these two aspects. Currently, almost all state-of-the-art models for ATS tasks are based on DL architecture. However, in the field of DL-based abstractive ATS, a comprehensive literature survey is still lacking. Accordingly, their research paper fills that gap by providing researchers with a comprehensive survey of DL-based abstractive ATS. They begin by providing an overview of abstractive ATS and DL techniques. Then, they summarized several typical abstractive ATS frameworks. Following that, they compared several popular datasets that were commonly used for training, validation, and testing. They have examined the performance of several common abstractive ATS systems on common datasets. Finally, they have outlined some future research trends and highlighted some open challenges in the abstractive ATS tasks. They have argued that these investigations will provide new insights into DL-based abstractive ATS for researchers.According to [83], ATS systems benefit users in a variety of ways, including reducing summary reading time, making research selection easier, increasing effectiveness, and personalized summaries that can be useful in question answering systems that provide individualized information. Thus, the proposed model was aimed at developing abstractive summaries of documents that assist users in understanding the gist by analyzing the content using approaches such as NLP and DL. Text summarization methods can achieve abstractive summarization of multiple documents. Seq-to-Seq and (Bi-LSTM) encoder and decoder with attention mechanism, pointer-generated network (PGN), and coverage mechanism (CM) algorithms were used. The disadvantages of Bi-LSTM, such as replacing one word with another and having redundant words, were addressed using PGN and CM. The proposed model summarizes multiple documents and provides a relevant and precise abstractive summary.According to [84], in the last few years, significant progress in the field of ATS has been made due to the development of NLP, data collection, and analysis opportunities. While many researchers continue to focus on improving extractive summarization, many have shifted their focus to abstractive techniques, with several breakthroughs in this area recently. Despite all the research in the field of ATS, current summarizers are far from perfect, and many problems remain unsolved. RNN encoder-decoder structures, for example, which are currently considered cutting-edge, still fail to encode long documents. Many current summarizers still rely on identifying frequent word sequences with no semantic processing, which is especially noticeable in query-focused or guided summarization, which requires “understanding” of the documents. Advances in this area could eventually lead to highly personalized, interactive summaries that are tailored to the specific needs of the user. Furthermore, the field is severely lacking in high-quality summarization datasets, particularly in domains other than science and in languages other than English, which slows and complicates future progress. Finally, there is a need to improve methods for evaluating ATS systems using commonly used metrics. The Rouge is far from perfect in many ways, and as more advanced, focused summaries emerge, the need for more consistent evaluation methods will become essential.According to [44], because of their ability to capture spatial-spectral feature representations, convolutional neural networks (CNNs) are gaining popularity in hyperspectral (HS) image classification. Nonetheless, their ability to model relationships between samples is limited. Graph convolutional networks (GCNs) have recently been proposed and successfully applied in irregular (or nongrid) data representation and analysis, overcoming the limitations of grid sampling. They thoroughly investigate CNNs and GCNs (qualitatively and quantitatively) in terms of HS image classification in this article. Traditional GCNs typically have a high computational cost due to the construction of the adjacency matrix on all of the data, especially in large-scale remote sensing (RS) problems. To that end, they created a new minibatch GCN (referred to as miniGCN hereafter), which allows them to train large-scale GCNs in a minibatch fashion. More importantly, their miniGCN can infer from sample data without retraining networks and improve classification performance. Furthermore, because CNN’s and GCNs can extract different types of HS features, fusing them is an obvious way to overcome the performance bottleneck of a single model. Because miniGCNs can perform batch-wise network training (allowing the combination of CNNs and GCNs), they investigated three fusion strategies to measure the obtained performance gain: additive fusion, elementwise multiplicative fusion, and concatenation fusion. Extensive experiments on three HS datasets show the advantages of miniGCNs over GCNs, as well as the superiority of the tested fusion strategies over single CNN or GCN models [44]. Finally, they recommended future researchers investigate the possible combination of different deep networks and our miniGCNs, as well as develop more advanced fusion modules, such as weighted fusion, to fully exploit the rich spectral information contained in HS images.According to [85], as a critical technique for the exploitation of hyperspectral data, blind hyperspectral unmixing (HU) aims to decompose mixed pixels into a collection of constituent materials weighted by the corresponding fractional abundances. Nonnegative matrix factorization- (NMF-) based methods have become increasingly popular for this task in recent years, with promising results. Among these methods, two types of abundance properties, namely, sparseness and structural smoothness, have been investigated and shown to be important for blind HU. All of the previous methods, however, ignore another important insightful property of natural hyperspectral images (HSI), nonlocal smoothness, which means that similar patches in a larger region of an HSI share the same smoothness structure. Based on previous efforts on other tasks, such a prior structure reflects intrinsic configurations underlying an HSI and is thus expected to significantly improve the performance of the investigated HU problem. They first considered such a prior in HSI by encoding it as the nonlocal total variation (NLTV) regularizer in this paper. Furthermore, by thoroughly investigating the intrinsic structure of HSI, they generalized NLTV to nonlocal HSI TV (NLHTV), making the model more suitable for the bind HU task. They proposed novel blind HU models named NLTV/NLHTV and log-sum regularized NMF (NLTV-LSRNMF/NLHTV-LSRNMF) by incorporating these two regularizers, along with a nonconvex log-sum form regularizer characterizing the sparseness of abundance maps, to the NMF model. An efficient algorithm based on an alternative optimization strategy (AOS) and alternating direction method of multipliers is designed to solve the proposed models (ADMM). Extensive experiments on simulated and real hyperspectral datasets demonstrate the superiority of the proposed approach over competing approaches for the blind HU task.According to [86], because of recent advances in DL and the availability of large-scale corpora, ATS has achieved impressive performance. The key points in ATS have been estimating information salience and producing coherent results. Several DL-based approaches have recently been developed to better consider these two aspects. However, a comprehensive literature review for DL-based ATS approaches is still lacking. The purpose of the paper was to provide a comprehensive review of significant DL-based approaches that have been proposed in the study concerning the concept of generic ATS tasks, as well as a walkthrough of their evolution. The researchers began by providing an overview of ATS and DL. Comparisons of datasets, which were commonly used for model training, validation, and evaluation, were also provided. They have then summarized SDS approaches. Following that, an overview of MDS approaches was provided. They also examined the performance of popular ATS models on common datasets. For various ATS tasks, various popular approaches can be used. Finally, they have suggested potential research directions in this rapidly expanding field. The researchers hoped that this exploration would provide new insights into future DL-based ATS research.According to [87], the task of generating a summary, whether extractive or abstractive, has been studied in the paper using various approaches, including statistical, graph, and DL-based approaches. In comparison to traditional approaches, the DL has shown promising results, and with the advancement of different neural architectures such as the attention network (also known as the transformer), there are potential areas of improvement for the summarization task. The implementation of the transformer architecture and its encoder model “BERT” resulted in improved performance in downstream NLP tasks. The BERT is a bidirectional encoder representation modeled as a stack of encoders from a transformer. BERT comes in a variety of sizes, including BERT-base with 12 encoders and BERT-larger with 24 encoders, but they focused on the BERT-base for this study. The objective of the paper was to conduct a study on the performance of variants of BERT-based models on text summarization using a series of experiments and to propose “SqueezeBERTSum,” a trained summarization model fine-tuned with the SqueezeBERT encoder variant, which achieved competitive Rouge scores while retaining the BERTSum baseline model performance by 98% while requiring 49% fewer trainable parameters.As described in the related works, future researchers should have to use the novel and recommended state-of-the-art techniques to develop such an ATS system for different languages accordingly, because the performance of every ATS system depends on the techniques and extraction rates too. Table2 shows the summary of ATS works for the Ethiopian languages.Table 2
Summary of the related work.
Author(s)The objective of the studyUsed methodologyPerformanceRecommendations/remarks[78]To develop a topic-based Tigrigna language text summarizer using topic-based and WordNet.Topic-based and WordNet(i) The proposed approach yields the best result, which is 50.14% precision/recall of the original text, according to their evaluation of the proposed algorithm at a 25% extraction rate summarization.(i) They have advised future researchers to think about creating a standard Tigrigna corpus that can inspire, reduce corpus collection time, and be used by researchers to assess the performance of their system.[77]To explore and develop an automatic text summarizer for Tigrinya language news articles.OTS(i) In the trial, the system recorded recall, precision, and f-score values of 46%, 46%, and 46%, respectively, for the feature of word frequency.(ii) Recall, precision, and f-score values for the title word were 46%, 50%, and 48%, respectively, demonstrating the summarizer’s improvement using this technique.(i) They have advised using corpora, a standardized linguistic resource for Tigrinya text summarization that is essential for producing strong performance evaluations.[76]To develop an automatic single document Afan Oromo language text summarization.Word embedding(i) The results of the subjective evaluation were as follows: informativeness scores of 83.33%, referential integrity, and grammar score of 78.8%, and structure and coherence score of 76.66%.(ii) Using the information we acquired, this work also achieved 52.70% precision, 42.20% recall, and 46.80% f-score.(i) As it is a crucial component of continuing development and evaluation of the performances of the summarizers, they have advised the compilation of the corpus, including the techniques.[75]To build up a single document automatic summarizer for Afan Oromo language news text.OTS(i) The objective evaluation’s result indicates that the three summarizers methods 1 and 2 and method 3 registered f-measure values of 34%, 47%, and 81%, respectively.(ii) This means that method 3 outperformed methods 1 and 2 by 47% and 34%.(iii) Additionally, according to the subjective evaluation results, the three summarizers’ (method 1, method 2, and method 3) performances in terms of informativeness, linguistic quality, coherence, and structure were 34.37%, 37%, and 62.5%; 59.37%, 60%, and 65%; and 21.87%, 28.12%, and 75%, respectively.(iv) The outcomes are consistent across subjective and objective evaluations. Other summarizers are outperformed by summarizer method 3, which combines term frequency and enhanced position approaches. Method 2 is next in performance.(i) They have proposed a balanced well-prepared corpus which is an essential aspect of further evaluation of the performances of the summarizers.[74]To develop document summarization for the Afan Oromo language based on the query entered by the user(s).QB framework has used the TF-IDF term weight methodology(i) The evaluations showed that the proposed system registered f-score of 90%, 91%, and 93% at a summary extraction rate of 10%, 20%, and 30%, respectively.(ii) When both methods were used together, the proposed system’s informativeness and coherence registered their best performance summary of 51.67%, 56.67%, and 54.17% average scores on five scale measures at an extraction rate of 10%, 20%, and 30%, respectively.(i) To create paragraphs that have a semantically accurate and descriptive flow of thoughts on the system’s summarized results, they have recommended that more analysis work on semantics analysis of the structure be done.[67]To design an algorithm that can summarize a document by its performance both objectively and subjectively in Afan Oromo language.Frequency and sentence position methods(i) The researchers did not provide quantifiable performance data for the system.(i) Future gabs were not predicted by the researchers.[64]To develop Amharic language text summarization for news items posted on social media (Facebook and Twitter).K-means algorithm(i) The clustered group of protest posters in experiment one had the greatest f-score score of 87.07% for extraction rate at 30%.(ii) The greatest f-score in the second trial is 84% for extraction rate at 30% in drought post groups.(iii) For the sports post groups in the third experiment, the highest f-score is 91.37%, and for the summary post texts in the fourth trial, the greatest f-score is 93.52% for extraction rate at 30%.(i) Instead of manually preparing the corpus of independently posted news items on summarization for other local languages, use the suggested algorithm to summarize the recognizing the news item posted texts automatically grouped on social media post news.(ii) Applying for the tasks of multiple documents uploaded Amharic language texts and sequential update summarizing tasks were the recommended prospective future work to research social media posts.(iii) Use the dictionary file and additional Amharic language lexicon rules to manage the over and under stemming of Amharic language terms.[73]To design and develop an SDS for Afan Oromo language news text.Sentence scoring method(i) Nine experimental circumstances were used to test the system both subjectively and objectively.(ii) The summary’s structure was subjectively evaluated for referential clarity, informativeness, grammatical correctness, and coherence.(iii) They also looked for any redundancies. So, at 20%, 30%, and 40% extraction rate grammatical correctness is 90%, 90%, and 92%, respectively, concerning redundancy at 20%, 30%, and 40% extraction rate performance of the summarizer system was 72%, 82%, and 84%, respectively.(iv) Additionally, referential clarity scores at 20%, 30%, and 40% extraction rates are 66%, 74%, and 86%, respectively.(v) Coherency of the summary evaluation done at extraction rates of 20%, 30%, and 40%, respectively, was 62%, 66%, and 72%.(vi) Furthermore, the performance of the automatic summary was 74%, 78%, and 86% in terms of informativeness at extraction rates of 20%, 30%, and 40%.(vii) The system performed at a rate of 86.1% when the three metrics recall, precision, and f-score were computed using that objective evaluation.(i) For additional research, they recommended developing or creating a well-organized, standard Afan Oromo language text corpus, as well as making a persuasive performance evaluation that will increase the performance of the systems.[72]To explore the possibility of developing a query-based, single document, extractive summarization system for Afan Oromo language news text.QB (VSM and the position method)(i) The evaluations’ results demonstrated that, when VSM is combined with the position technique, the suggested system reported f-scores of 82%, 782%, and 82% at rates of summary extraction of 10%, 20%, and 30%, respectively.(ii) Additionally, when both approaches were employed together, the suggested system’s informativeness and coherence both recorded their best performance summaries of 59%, 77%, and 91% average scores on five scale measures.(i) They recommended that to develop a conclusive performance evaluation for additional research, a consistent and well-prepared Afan Oromo language text corpus was needed.[71]To investigate the way of designing and developing Afan Oromo language news text summarization based on sentence selection functions.OTS C# version open source has been selected as a tool to develop the system(i) The subjective evaluation focuses on the summary’s structure, including referential integrity, lack of repetition, coherence, and informativeness.(ii) Metrics like precision, recall, and f-score are used in the objective evaluation for evaluation.(iii) The evaluation’s subjective findings are 68% coherence, 75% referential integrity, and 88% informativeness.(i) The creation of the Afan Oromo language text summarizer and future evaluation of the summarizer’s performance needs the use of a standard and well-prepared corpus.[89]To develop an efficient language independent text summarization.GB(i) On the 264 documents from the DUC2002 document set, the suggested approach yields an average recall-oriented understudy for (ROUG)-1 result of 52.38%.(i) To enhance the functionality of the system, they recommended future researchers employ various threshold values, such as tf∗idf.[69]To develop automatic Amharic language text summarization.LSA: using TopicLSA and LSAGraph(i) As more of the retained dimensions are used, LSAGraph performs better even without the document genre component.(ii) In other words, when more than 90% of the dimensions are kept, it achieves an f-score that is comparable to that of its non-LSA counterparts.(i) They suggested that language preprocessing exercises be used to prepare the corpus for upcoming summary research.(ii) Additionally, they suggested that the strategy be used with other languages.[90]To develop TB Amharic language text summarization.PLSA(i) For summaries of 20%, 25%, and 30% extraction rates, they have assessed the proposed algorithms’ precision/recall.(ii) The best outcomes were 45.51% at 20%, 48.49% at 25%, and 52.01% at 30%.(i) Future studies ought to look into the best word weighting strategies for probabilistic topic models.(ii) Additionally, they suggested that the size of the dataset be taken into account.[68]To investigate the application of ML and technique (NB) for automatic text summarization.ML (including NB)(i) Except for the location feature, which results in a precision of 75%, recall of 74.90%, and classification accuracy of 86.03% in predicting the summary sentences, the individual features that were taken into account in predicting the classes tended to predict the nonsummary class.(i) They suggested that study in the field of ATS would be made easier by the availability of standard Amharic language corpora for text summarization.[65]To develop Amharic language single text summarizer using open text summarization.OTS (TF and sentence length)(i) The experiment was carried out on both systems by producing 90% summaries for each news article at extraction rates of 10%, 20%, and 30%.(ii) Accordingly, the first experiment’s highest score was 75.65% at a 30% extraction rate for middle-sized articles and a 66.23% corpus average score was achieved, while experiment two’s highest score was 72.83% at a 30% extraction rate for large-size news articles and a 72.37% corpus average score.(i) They recommended that a comprehensive list of Amharic language lexicons be created and made accessible.(ii) For the Amharic language, a uniform list of stop words, synonyms, abbreviations, and compound terms should be created.[91]To investigate the applications of domain-based approaches to an automatic Amharic language text document summarization using an NLP parser.NLP parser(i) Precision, recall, and f-measure, three often used evaluation metrics, have been computed.(ii) But, they did not indicate the exact quantitative value for all the metrics.(i) They suggested that in addition to text documents, a text summary model be created for all document kinds, including graphs, photos, pictures, videos, and other formats.[92]To develop Amharic language abstractive text summarization.Novel DL(i) On a set of 100 test phrases,BELU=33.11%, ROUG−1f=20.51%, ROUG−2f=8.59%, and ROUG−Lf=14.76%.(ii) A recommendation for using DL models that can be applied to various NLP jobs serves as their conclusion.[88]To develop GB automatic Amharic language textsummarizer.GB(i) The link evaluation methods and sentence centrality measurements have been combined in twelve studies to provide system summaries with compression rates of 10%, 20%, and 30%.(i) They recommended that one of the key goals of their future work should be to identify a way to handle different circumstances.(ii) Additionally, they have recommended future researchers use standardized corpora to test their performances.[63]To develop a prototype for an Amharic language text summarizer using extractive techniques for legal judgments.Extrinsic evaluation technique(i) They employed compression rates of 20% and 10%.(ii) The average precision at 20% was 33.9%, whereas the average precision at 10% is 39%, indicating that the system performs more accurately at 10% than at 20%.(iii) The average precision of the random summary at 10% was 27% and that of the system summary is 39% which shows that the system summary can extract more relevant sentences than the random summary.(i) They recommended that the system’s effectiveness will depend on the availability of corpora and linguistic preparation.[62]To develop a system that prepares a summary of news text by extracting the sentences with vital substances with them.Developed by integrating selected statistical and NLP approaches(i) After weights were adjusted, the evaluation of the system produced 70.4% precision and 58% recall while reducing the size of the news to 38.5%.(ii) The weights of the diagnostic units in the model were changed using four news items in 124 iterations, influencing six of the eight diagnostic units utilized in the extraction method.(i) To enhance the performance of the system, they recommended that adding more investigative units for NLP, such as nouns and adjectives will increase the performance of such systems.[93]To develop an abstractive text summarizer for Amharic language.UNL(i) Since they have employed a subjective evaluation of summary sentences, the evaluation’s outcomes are encouraging.(ii) They recommended that the morphology and grammar principles employed in the language be viewed as general rules to apply them to more complex sentences and that an ML approach be taken into consideration for activities relating to the universal networking language.[79]To explore the capability of the OTS for Amharic multiple document news text summarization.OTS(i) At a 20% extraction rate, the measure and the summarizer perform with an average f-measure of 52.14%, a 56.31% f-score at a 30% extraction rate, and a 57.58% f-score at a 40% extraction rate.(i) Since the proposed method, an extractive-based text summarizer, lacks coherence, other advanced systems, such as abstractive-based summarizers, adding extra statistical features, using complete corpora, and trying various methods like anaphora resolution, lexical chains, and discourse structure, have been advised.The table summarizes that the DL approaches are currently referred by researchers over ML approaches due to their efficiency in learning from large-scale corpora in unlabeled text.


## 6. Performance Evaluation of ATS

The goal of ATS is to help users condense all important information that needs to be summarized. Although many summarization techniques can be used to create summaries from texts and documents, these techniques are still limited to extracting specific parts of the original text and concatenating them into a shorter text, abstraction, or broad paraphrasing of the original material. The ultimate goal of any ATS system should be to be able to summarize texts as close to a human-generated summary as possible.However, existing ATS systems face significant challenges to achieve this goal. Some common difficulties include lag, “anaphora” problems, and “cataphora” problems. The evaluation of text summarization is a challenging task. This task is difficult for machines to identify important key phrases or contents that add value to the summary. Placing key phrases has changed the meaning of the summary depending on the context, and it is difficult to find this relevant information. As a result, for reliable and effective evaluation, automatic evaluation measures are required. Following a review of previously researched papers on text summarization topics, several methods for summarization measurement are determined. The following are the evaluation measurement metrics for the ATS domain [40].


### 6.1. Extrinsic Evaluation

The quality of an ATS-generated summary is determined by how it influences other activities such as text categorization, information retrieval, and question answering. In summarizing, it is considered good if it aids the previously mentioned other activities. Extrinsic evaluation can be approached in a variety of ways. The relevance assessment determines whether or not the text is relevant to the topic, and reading comprehension determines whether or not the text can answer multiple-choice assessments.


### 6.2. Intrinsic Evaluation

The intrinsic evaluation determines the summary’s quality based on the comparability of machine-generated and human-generated summaries. A good summary is judged on two important criteria: quality and information. Human experts may be required to assess machine-generated summaries using a variety of quality measures. Readability, nonredundancy, structure, and coherence are some quality metrics, and some others include referential clarity, conciseness and focus, and content coverage, among others. Precision, recall, and the f-score are all useful measures for intrinsically evaluating summaries. Researchers must plan for the possibility of comparing human-generated and automatically generated summaries. With the previously mentioned evaluation metrics, it is also possible that the two summaries produce different evaluation results despite being equally good. Among the several approaches used to assess the performance of text summarization systems, objective evaluation plays a significant part in determining the efficiencies, qualities, and performances. It is also one of the evaluation methods used in this study to determine the efficiency of the summarizers. Precision, recall, and f-score were utilized to assess summarization performances, which are among the most regularly used metrics for this purpose. Equations (1), (2), (3), and (4) define these criteria.
(1)
Precision metric: the precision metric determines whether or not the percentage of sentences selected by humans and computers is correct. The formula shows how to calculate the precision metric by dividing the total number of sentences between two summaries by the number of sentences in the system summary.(1)PrecisionP=Sumr∩SumsSums(2)
Recall metric: the recall metric determines how many sentences are selected by humans by the system. The number of sentences in both the reference and system summaries is divided to arrive at the following equation.(2)RecallR=Sumr∩SumsSumr(3)
f-score metric: this metric combines recall and precision metrics. An F-score metric is the arithmetic mean of precision and recall.(3)f−score=2∗R∗PR+P(4)
BLEU: the bilingual evaluation understudy (BLEU) evaluation metrics assess machine translation system output quality in terms of reference translation. The main task of this metric is to count the number of N-gram matches found independently between the system and the reference translations.(4)BLEU=min1,outlengthreferencelength∏i=14precisioni14(5)
Rouge metric: recall-oriented understudy for gisting evaluation (ROUGE) is a set of ATS and machine translation evaluations. It compares an automatically generated summary or translation to a predefined set of summaries, such as human-generated summaries. Rouge is made up of five different measurements: Rouge-n, Rouge-l, Rouge-w, Rouge-s, and Rouge-su


## 6.1. Extrinsic Evaluation

The quality of an ATS-generated summary is determined by how it influences other activities such as text categorization, information retrieval, and question answering. In summarizing, it is considered good if it aids the previously mentioned other activities. Extrinsic evaluation can be approached in a variety of ways. The relevance assessment determines whether or not the text is relevant to the topic, and reading comprehension determines whether or not the text can answer multiple-choice assessments.


## 6.2. Intrinsic Evaluation

The intrinsic evaluation determines the summary’s quality based on the comparability of machine-generated and human-generated summaries. A good summary is judged on two important criteria: quality and information. Human experts may be required to assess machine-generated summaries using a variety of quality measures. Readability, nonredundancy, structure, and coherence are some quality metrics, and some others include referential clarity, conciseness and focus, and content coverage, among others. Precision, recall, and the f-score are all useful measures for intrinsically evaluating summaries. Researchers must plan for the possibility of comparing human-generated and automatically generated summaries. With the previously mentioned evaluation metrics, it is also possible that the two summaries produce different evaluation results despite being equally good. Among the several approaches used to assess the performance of text summarization systems, objective evaluation plays a significant part in determining the efficiencies, qualities, and performances. It is also one of the evaluation methods used in this study to determine the efficiency of the summarizers. Precision, recall, and f-score were utilized to assess summarization performances, which are among the most regularly used metrics for this purpose. Equations (1), (2), (3), and (4) define these criteria.
(1)
Precision metric: the precision metric determines whether or not the percentage of sentences selected by humans and computers is correct. The formula shows how to calculate the precision metric by dividing the total number of sentences between two summaries by the number of sentences in the system summary.(1)PrecisionP=Sumr∩SumsSums(2)
Recall metric: the recall metric determines how many sentences are selected by humans by the system. The number of sentences in both the reference and system summaries is divided to arrive at the following equation.(2)RecallR=Sumr∩SumsSumr(3)
f-score metric: this metric combines recall and precision metrics. An F-score metric is the arithmetic mean of precision and recall.(3)f−score=2∗R∗PR+P(4)
BLEU: the bilingual evaluation understudy (BLEU) evaluation metrics assess machine translation system output quality in terms of reference translation. The main task of this metric is to count the number of N-gram matches found independently between the system and the reference translations.(4)BLEU=min1,outlengthreferencelength∏i=14precisioni14(5)
Rouge metric: recall-oriented understudy for gisting evaluation (ROUGE) is a set of ATS and machine translation evaluations. It compares an automatically generated summary or translation to a predefined set of summaries, such as human-generated summaries. Rouge is made up of five different measurements: Rouge-n, Rouge-l, Rouge-w, Rouge-s, and Rouge-su


## 7. Results and Discussions

In the ATS, the task of generating a summary, whether extractive or abstractive, has been studied using various approaches, including statistical ML and DL-based approaches. As a result, DL approaches have outperformed traditional approaches, and with the advancement of different neural architectures such as the attention network (also known as the transformer), there are potential areas of improvement for the summarization tasks. As shown in Figures5 and 6, the objective summarizations of the texts were evaluated by comparing the system-generated summary with the reference summary, and the summarizations’ performance was quantified using precision, recall, and f-score. As the extraction rate increases, so does the average f-score result for each extractive summary. In the study results of [65, 69, 79] concerning text summarization, evidence for improved performance due to the increased extraction rates.Figure 5
Comparison of similar extraction rates (20%, 30%, and 40%) (in objective evaluation).Figure 6
Comparison of similar extraction rates (10%, 20%, and 30%) (in objective evaluation).As shown in Figure7, it can be observed the ATS systems of SSM [73] and OTS [79] which was proposed by using 20%, 30%, and 40% extraction rates. Accordingly, by using 20%, 30%, and 40% extraction rates, the performance will be 31%, 34%, and 35%, respectively. Similarly, in Figure 8, it can be observed the ATS systems of OTS [65], QB with VSM [72], QB [74], OTS [77], and GB [88], which were proposed by using 10%, 20%, and 30% extraction rates. Accordingly, by using 10%, 20%, and 30% extraction rates, the performance will be 21%, 35%, and 44%, respectively. This result indicates that the performance of the system will depend on the extraction rates accordingly. As part of this review work, the performances of 90%, 91%, and 93% were achieved by using the QB [74] approach and with an extraction rate of 10%, 20%, and 30%, respectively. Finally, the implementation of the transformer architecture and its encoder model “BERT” resulted in improved performance in downstream NLP tasks. Using DL approaches as recommended, which achieved competitive Rouge scores while retaining the “BERTSum” baseline model performance of 98% while using 49% fewer trainable parameters, ATS system performance can be improved [87]. Similarly, the researchers have evaluated their ATS works by considering subjective evaluation criteria such as coherence, referential integrity, informativeness, and grammatical correctness. Accordingly, it is better to consider the objective evaluations to improve the performance of the ATS for Ethiopian languages.Figure 7
Distribution of implemented approaches with extraction rates (20%, 30%, and 40%) (in objective evaluation).Figure 8
Distribution of implemented approaches with extraction rates (10%, 20%, and 30%) (in objective evaluation).


## 8. Conclusion and Recommendation

ATS systems have emerged as a technology to address this urgent and pressing problem due to the difficulties of manual text summarizing the enormous amount of textual content on the Internet or in various archives. The machines still struggle to comprehend and recognize the text’s “most important” contents (i.e., the importance of a text varies with its type, application domain, and user preference). Although text summarizing is an old issue, academics are still becoming interested in it. However, overall text summarizing performance is only average, and the summaries produced are not always accurate. Researchers are therefore working to enhance current text-summarizing techniques. It is also a top priority to create summaries that are resilient and of higher quality using unique summarization techniques. To perform better, ATS should be made more intelligent by fusing it with other integrated systems. To summarize and reduce the volume of text, ATS is a prominent area of research that is widely applied and integrated into various applications. In this work, the researcher provides a review of the broad ATS domains in several phases, including the fundamental theories with prior research backgrounds, dataset inspections, feature extraction architectures, significant text summarization algorithms, performance measurement matrices, and difficulties of current architectures. Additionally, the work discusses the current drawbacks and difficulties associated with ATS algorithms and methodologies, which should serve as motivation for future work. This work also provides a thorough analysis and overview of the various ATS components for the Ethiopian languages.As it can be observed from the evaluation results of ATS research works, the performance can be improved by using the recommended techniques and extraction rates accordingly. In the topic of ATS research, future research work should have to consider the following:(i)
Solving feature-related problems, such as selecting features, discovering new features, optimizing commonly used features, feature engineering, using features for semantics and linguistic features, finding features to produce coherence sentences, and adding grammatical features(ii)
Preprocessing the dataset using appropriate stemming, tokenization, and stop word removal is required(iii)
The POST is also required, specifically to categorize word classes such as nouns, verbs, and adjectives(iv)
Collaborative statistical techniques, FL-based techniques, and ML are all difficult to try for extractive summaries. To produce a better summary, develop an MDS by combining the FL and TF-IDF methods with an ML approach(v)
In other cases, it is possible to develop the most recent method, narrative abstractive summarization, or improve the performance of narrative abstractive summarization by increasing coherence



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