Extractive Summarization using Extended T ext R ank Algorithm (2024)

Abstract: With so much information available online, it’s more important than ever to have reliable tools for summarizing text quickly and accurately. In this paper, we introduce a new way to improve the popular TextRank algorithm for extractive summarization. By adding a dynamic damping factor and using Latent Dirichlet Allocation (LDA) to enhance how text is represented, our method creates more meaningful summaries. We tested it with metrics like Pyramid, METEOR, and ROUGE, and compared it to the original TextRank. The results were promising, showing that our approach produces better summaries and could be useful for real-world applications like text mining and information retrieval.

## introduction there is a gigantic amount of data being generated on the internet in recent times. the amount of textual content generated in the form of legal documents, scripts, news articles, etc., is very vast. generating concise summaries of such vast textual sources manually is a very time-consuming and tedious task to do. hence, this gives rise to various approaches that help in generating automatic summaries through ats (automatic text summarization) systems. these ats systems help to extract important information from documents. the main aim of text summarization is to shorten the length of the source text while preserving all important sentences and the overall meaning of the source text. text summarization is divided into two types, mainly single-document summarization and multi-document summarization as mentioned in @xcite and @xcite there are three types of approaches that can be followed for generating ats systems as mentioned in @xcite . these approaches extractive summarization, abstractive summarization and hybrid summarization. the extractive method aims to extract the most important sentences from the input document. abstractive method on the other hand aims at generating sentences that differ from the original sentences in the input text and generate summary that coveys the entire meaning of the document. the above mentioned paper also explains the different methodologies that can be followed to model the ats systems. extractive ats systems can be modelled by various methods like statistical based, concept based, graph based, semantic based, etc methods. abstractive ats systems are modelled by using graph based, tree based, ontology based, etc methods including bert summarization which outperforms textrank summarization as seen in @xcite extractive summarizers are less complex and they preserve the fidelity of the original document. they are not prone to errors related to text generation and misinterpretation as they directly extract the highest-ranked sentences. @xcite gives a survey of extractive graphbased summarization and states how it outperforms other approaches. @xcite ) is a graph-based method for extractive text summarization that aligns with pagerank's ranking algorithm @xcite . the advantage of the textrank algorithm is that it is an unsupervised method and does not require any training to generate the summary. along with being unsupervised, textrank is also a language-independent method that generates a summary based on the occurrences of words. aligning with this graph-based method of text summarization, our proposed model extends the textrank algorithm by integrating lda topic modeling @xcite and @xcite ) and a dynamic damping factor as parameters to enhance the vector embeddings of the nodes of the graph and shows the results obtained by our model using various evaluation metrics like rouge-1, rouge-2, and pyramid that evaluate the scores based on measures like recall, preci-sion, and f1 score. ## literature survey given the large corpus of data being generated daily, it has become increasingly important to develop automatic text summarization models that can produce precise and concise summaries. @xcite initially proposed a graphbased ranking algorithm, textrank, which uses an unsupervised method for sentence extraction. this technique is an extension of google's renowned pagerank algorithm @xcite . one key advantage of this state-of-the-art method is its language independence, making it easily adaptable to different domains, genres, and languages. building on this, @xcite made further advancements by analyzing 75 articles extracted from the medium site. in their study, they combined a similarity matrix generated through bm25+ with the original textrank, normalizing the matrix by dividing it by the maximum similarity score in a given matrix. their primary focus was to enhance the mean f-score for all rouge metrics compared to both the original textrank algorithm and bm25+. their results showed an improvement of 1.654% and 0.413%, respectively. @xcite suggested that lda is an unsupervised generative probabilistic model for corpus modeling. they introduced various lda parameters such as gibbs sampling, expectation-maximization (em), and variational bayes inference (vb). @xcite evaluated the extractive summarization method and the efficacy of lda using latent semantic analysis (lsa) and rouge metrics to assess its accuracy and reliability. @xcite focuses on enhancing extractive text summarization by using topicbased sentence representation. @xcite used lexical centrality to determine sentence salience within a document. @xcite proposed deepchannel, an attention-based neural network that achieved robust results using only 1 % of the cnn/dailymail dataset. @xcite and @xcite introduced cnn based approaches to achieve high accuracy against benchmark datasets. @xcite focuses on text summarization methods using various llm models. @xcite used the webis-editorialsum-2020 dataset, containing 1,330 summaries for 266 articles. their approach involved two models for generating ex-tractive summaries. the first was textrank, which employed two similarity functions: lexical overlap and common named entities. the second model used bert-based embeddings and clustered segments using k-means, with two variants: extsum-xlnet and extsum-distilbert @xcite . @xcite contributed to the field by employing the sumsurvey dataset, a long-document abstractive summarization dataset composed of scientific survey papers. well-known evaluation metrics such as rouge and bert score were used as reference-based measures in extractive summarization. additionally, their abstractive summarization made use of unieval, with large language models (chatgpt, vicuna) serving as reference-based metrics. they also applied reference-free metrics, such as novel n-gram, to measure the abstractiveness of the summaries. in this research, we also use pyramid scores to evaluate the generated text summaries, as noted in @xcite . this paper introduces pyreval, which outperforms previously developed automated pyramid methods and sets a new benchmark for precision and accuracy in text summarization evaluation. ## evaluation the summary generated is evaluated against the reference summary from the dataset. various summary evaluation methods are available, such as the rouge metric (rouge-n and rouge-l scores), pyramid evaluation metric, and meteor (metric for evaluation of translation with explicit ordering). the evaluation is performed for multiple articles. ## conclusion we explored a new approach to text summarization by integrating textrank with latent dirichlet allocation (lda) to perform topical modelling and using a dynamically calculated damping factor. this approach demonstrated significant improvements over traditional textrank method. in this model we are using the semantic information extracted through lda topic modelling to capture the themes within a document. from the results summarized in the earlier section, it is clear that the proposed model works well with multi-sentence summaries (around 20% of original length). when we tried the model to generate smaller summaries (2 or 3 sentences long), the results were inferior to general textrank due topic relevance shifts are influenced by the damping factor. longer documents tend to have better results due to better captured topical coherence through lda. the dynamic damping factor can be adapted for abstractive summarization by guiding content selection, attention mechanisms, and counter decoder biases. in transformer-based models, it can scale attention scores, prioritize context-based more relevant input, and refine reward function during finetuning (if using reinforcement learning). this enhances topic relevance and coherence in abstractive summaries. the dynamic damping factor adapts to the nature of the input text and ensures a balanced representation of all important concepts. we can also try various embedding methods to further improve the accuracy of summarization.