Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment
One of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult and sometimes impossible. The goal of multi-docu...
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University of Qom
2020-09-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_1270_d874d641d3de3efad886d09ba7e820fa.pdf |
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| author | Ali Naserasadi |
| author_facet | Ali Naserasadi |
| author_sort | Ali Naserasadi |
| collection | DOAJ |
| description | One of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult and sometimes impossible. The goal of multi-document textual summarization is to produce a pre-defined length summary from input textual documents while maximizing documents’ content coverage. This paper presents a new approach for textual document summarization based on paraphrasing and textual entailment relations and formulating the problem as an optimization problem. In this approach the sentences of input documents are clustered according to paraphrasing relation and then the entailment score and final score of a fraction of the header sentences of clusters which have the best score according to the user query is calculated. Finally, the optimization problem is solved via greedy and dynamic programming approaches and while selecting the best sentences, the final summary is generated. The results of implementing the proposed system on standard datasets and evaluation via ROUGE system show that the proposed system outperforms the state-of-the-art systems at least by 2.5% in average. |
| format | Article |
| id | doaj-art-4a15662b49dc4bfc99dfd6ba54b7ce2d |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2020-09-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-4a15662b49dc4bfc99dfd6ba54b7ce2d2025-01-30T20:17:43ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752020-09-016218319810.22091/jemsc.2018.12701270Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual EntailmentAli Naserasadi0ComputerGroup,ZarnadIndustrialandMiningFaculty,ShahidBahonarUniversity,Kerman,IranOne of the most common problems with computer networks is the amount of information in these networks. Meanwhile searching and getting inform about content of textual document, as the most widespread forms of information on such networks, is difficult and sometimes impossible. The goal of multi-document textual summarization is to produce a pre-defined length summary from input textual documents while maximizing documents’ content coverage. This paper presents a new approach for textual document summarization based on paraphrasing and textual entailment relations and formulating the problem as an optimization problem. In this approach the sentences of input documents are clustered according to paraphrasing relation and then the entailment score and final score of a fraction of the header sentences of clusters which have the best score according to the user query is calculated. Finally, the optimization problem is solved via greedy and dynamic programming approaches and while selecting the best sentences, the final summary is generated. The results of implementing the proposed system on standard datasets and evaluation via ROUGE system show that the proposed system outperforms the state-of-the-art systems at least by 2.5% in average.https://jemsc.qom.ac.ir/article_1270_d874d641d3de3efad886d09ba7e820fa.pdftextual document summarizationdynamic programmingtextual entailment |
| spellingShingle | Ali Naserasadi Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment مدیریت مهندسی و رایانش نرم textual document summarization dynamic programming textual entailment |
| title | Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment |
| title_full | Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment |
| title_fullStr | Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment |
| title_full_unstemmed | Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment |
| title_short | Query-Based Extractive Multi-Document Summarization Using Paraphrasing and Textual Entailment |
| title_sort | query based extractive multi document summarization using paraphrasing and textual entailment |
| topic | textual document summarization dynamic programming textual entailment |
| url | https://jemsc.qom.ac.ir/article_1270_d874d641d3de3efad886d09ba7e820fa.pdf |
| work_keys_str_mv | AT alinaserasadi querybasedextractivemultidocumentsummarizationusingparaphrasingandtextualentailment |