Simplify: Automatic A rabic Sentence Simplification using Word Embeddings (2023)

Abstract: Automatic Text Simplification (TS) involves simplifying language complexity while preserving the original meaning. The main objective of TS is to enhance the readability of complex texts, making them more accessible to a broader range of readers. This work focuses on developing a lexical text simplification system specifically for Arabic. We utilized FastText and Arabert pre-trained embedding models to create various simplification models. Our lexical approach involves a series of steps: identifying complex words, generating potential replacements, and selecting one replacement for the complex word within a sentence. We presented two main identification models: binary and multi-complexity models. We assessed the efficacy of these models by employing BERTScore to measure the similarity between the sentences generated by these models and the intended simple sentences. This comparative analysis evaluated the effectiveness of these models in accurately identifying and selecting complex words.

## introduction automatic text simplification (ts) aims to make text less linguistically complex without changing its meaning or original information. this involves rewriting a complex text by performing various edit operations such as deletion, replacing words, splitting sentences, and changing the order of words. these actions are part of the ts natural language processing task @xcite . ts can benefit individuals who struggle with reading and writing, such as those with low literacy skills, dyslexia, or learning a new language. different simplification techniques can be employed depending on the desired purpose and the end user. additionally, ts can enhance written communication by ensuring that the target audience comprehends the intended message. @xcite . in addition, automated systems for simplifying text can help make the language more accessible to individuals who are not fluent in it or have limited proficiency. detecting text complexity is crucial in ts systems as it helps determine if the text needs to be simplified. it is also helpful in evaluating the results generated by the simplification system. ts systems primarily depend on syntax or lexical simplifications. @xcite . text simplification is related to techniques such as creating paraphrases, summarizing text, and machine translation in natural language processing (nlp). many strategies and evaluation methods used by text simplification are derived from these areas. in the past, rule-based syntactic simplification was used as a pre-processing step to improve various nlp tasks like parsing and formulating questions. @xcite . arabic is a widely spoken language consistently listed as one of the top 10 most spoken languages. this emphasizes the importance of incorporating different natural language processing tasks for arabic @xcite . we utilized the latest technologies in the field of nlp to carry out a straightforward simplification task. we developed two models for identification purposes: one that categorizes text as either complex or non-complex and another that classifies text into various levels of complexity. as a result, we utilized bert @xcite and fasttext @xcite to create the simplification model. we assessed the simplification phase using bertscore @xcite , which involved the two identification models. furthermore, we conducted a manual evaluation to ensure the quality of the simplified text. ## related work unlike english and other languages, only a few researchers have explored arabic automatic text simplification. in @xcite , they presented a text simplification tool named "albaseet". the tool's structure consisted of four main stages: complexity assessment, lexical simplification, syntax simplification, and diacritization. they followed the ls-pipeline approach to simplify the text and produced synonyms by creating a new vocabulary or utilizing arabicwordnet @xcite . the second attempt to construct an arabic ats was made by @xcite . their semiautomatic simplification approach was meant to simplify modern arabic fiction; a linguist applied actfl (american council on the teaching of foreign languages) language proficiency requirements for simplifying five arabic books using a web-based tool. they intended to create a readability measurement identifier using various machine learning classifiers to develop a graded reader scale of four levels. in @xcite , a method for identifying and visualizing complex words is presented. the authors' method combines lexical and syntactic analysis techniques, such as part-of-speech tagging and dependency parsing, to extract relevant information and create visualizations highlighting individual words' complexity. a system was proposed in (khallaf, 2023) that utilizes linguistic resources and rule-based transformations to identify complex linguistic structures and simplify them accordingly. ## simplification approach there are three stages involved in simplifying complex sentences. initially, we need to recognize the complex words used in the sentence. after identifying these complex words, we generate alternative options for them that are simpler and more comprehensible. these alternatives can include synonyms, definitions, or rephrasing of the original word. ultimately, we choose the most appropriate replacement for every intricate term, considering the surrounding context and the overall message conveyed in the text. ## evaluation & results in order to evaluate our models, we needed a parallel corpus. a parallel corpus is a collection of complicated texts and their simplified versions in the same language. to the best of our knowledge, there is only one available parallel corpus for the arabic language @xcite . the corpora are in different sizes. the small size contains 8 sentence pairs, the medium-sized size contains 69 sentence pairs, and the large contains 765 sentence pairs. ## conclusion and futute work to conclude, we endeavored to develop a lexical text simplification system for arabic. we introduced two models for identification: the binary model and the multi-comp model. furthermore, we suggested several simplification approaches utilizing fasttext and arabert embeddings. our perception of the lexical system restrictions is based on the fact that certain of the generated sen-tence structures need to be better-formed, and the system can incorrectly recognize complex words from simple ones in the cwi phase. in the future, it would be beneficial to utilize more recent models for evaluation.