A new graph-based extractive text summarization using keywords or topic modeling

Belwal, Ramesh Chandra; Rai, Sawan; Gupta, Atul

doi:10.1007/s12652-020-02591-x

Cited by 37 publications

(14 citation statements)

References 52 publications

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“…A keyword-based summary was then successfully created and sentiment analysis was created for the keywords. Another example of using a graph representation is in [13]. The authors assigned weight to the edges of the graph, on the basis of which the similarity in the graph was found.…”

Section: Related Workmentioning

confidence: 99%

A New Method for Graph-Based Representation of Text in Natural Language Processing

2023

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Natural language processing is still an emerging field in machine learning. Access to more and more data sets in textual form, new applications for artificial intelligence and the need for simple communication with operating systems all simultaneously affect the importance of natural language processing in evolving artificial intelligence. Traditional methods of textual representation, such as Bag-of-Words, have some limitations that result from the lack of consideration of semantics and dependencies between words. Therefore, we propose a new approach based on graph representations, which takes into account both local context and global relationships between words, allowing for a more expressive textual representation. The aim of the paper is to examine the possibility of using graph representations in natural language processing and to demonstrate their use in text classification. An innovative element of the proposed approach is the use of common cliques in graphs representing documents to create a feature vector. Experiments confirm that the proposed approach can improve classification efficiency. The use of a new text representation method to predict book categories based on the analysis of its content resulted in accuracy, precision, recall and an F1-score of over 90%. Moving from traditional approaches to a graph-based approach could make a big difference in natural language processing and text analysis and could open up new opportunities in the field.

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Section: Related Workmentioning

confidence: 99%

A New Method for Graph-Based Representation of Text in Natural Language Processing

2023

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“…The findings of this study demonstrate that this approach, with a stronger emphasis on subtopics, outperforms conventional topic modeling and deep learning approaches in unsupervised extractive summarization. The graph-based summarization technique proposed in the study in [ 33 ] takes into account both the resemblance among individual statements and their relation to the entire document. This approach employs topic modeling to determine the pertinence of specific edges to the topics discussed in the text, as well as a semantic measure to evaluate the similarity between nodes.…”

Section: Related Workmentioning

confidence: 99%

CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19

Karotia

Susan

2023

J Supercomput

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The number of research articles published on COVID-19 has dramatically increased since the outbreak of the pandemic in November 2019. This absurd rate of productivity in research articles leads to information overload. It has increasingly become urgent for researchers and medical associations to stay up to date on the latest COVID-19 studies. To address information overload in COVID-19 scientific literature, the study presents a novel hybrid model named CovSumm, an unsupervised graph-based hybrid approach for single-document summarization, that is evaluated on the CORD-19 dataset. We have tested the proposed methodology on the scientific papers in the database dated from January 1, 2021 to December 31, 2021, consisting of 840 documents in total. The proposed text summarization is a hybrid of two distinctive extractive approaches (1) GenCompareSum (transformer-based approach) and (2) TextRank (graph-based approach). The sum of scores generated by both methods is used to rank the sentences for generating the summary. On the CORD-19, the recall-oriented understudy for gisting evaluation (ROUGE) score metric is used to compare the performance of the CovSumm model with various state-of-the-art techniques. The proposed method achieved the highest scores of ROUGE-1: 40.14%, ROUGE-2: 13.25%, and ROUGE-L: 36.32%. The proposed hybrid approach shows improved performance on the CORD-19 dataset when compared to existing unsupervised text summarization methods.

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“…El-Kassas et al [38] proposed EdgeSumm, combining many methods to leverage the strength and reduce the weakness. Belwal et al [39] again proposed a graph-based method, and this time author has added extra parameters to calculate the similarity between a node to the entire text.Survey and reviews done by Widyassari et.al [40],El-Kassas et al [41] and Yadav et al [42] were peculiar helpful to gather lot of useful data.…”

Section: Related Workmentioning

confidence: 99%

Extractive Text Summarization Using Formality of Language

Mehta,

Bharti,

Doshi

2023

Preprint

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Automatic text summarization is a more than decade-old research method to get a summary from the larger descriptive text, and the cam- paign to find the best approach is still going on. Much research has been done, and new techniques are emerging to get a summary from the text document. Many unsupervised methods do not give justice to the lan- guage with high inflections. We aim to provide a unique technique that gives justice to complex morphological language. We present a novel method, the formality of a language, to generate a summary. We have used an already researched formality formula to measure the formality of language but customized it to suit an Indo-Iranian language family. We have proved that our redefined formula works with a 7.28% higher formality score than informal language. We also ran a statistical sig- nificance test to confirm the results. We apply this formula to measure whether the sentence is formal or informal. Our customized formula mix- ture with other measures like Shannon entropy score, and the presence of numeric values, we generate a score of a sentence. Based on scoring, we generate a text summary. We experimented formula on the generated Gujarati corpus and collected the results. We compared our technique with similar methods on ideal compression ratios like 20% and 30%. We got the highest accuracy with an increase of 14.63% compared to other methods on rouge-1 with a 30% compression ratio and a 28.60% increase in rouge-1 with a 20% compression ratio. Detailed experiment values with all the results have been provided for further study and research.

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A new graph-based extractive text summarization using keywords or topic modeling

Cited by 37 publications

References 52 publications

A New Method for Graph-Based Representation of Text in Natural Language Processing

A New Method for Graph-Based Representation of Text in Natural Language Processing

CovSumm: an unsupervised transformer-cum-graph-based hybrid document summarization model for CORD-19

Extractive Text Summarization Using Formality of Language

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