Features in extractive supervised single-document summarization: case of Persian news.
Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either abstractive or extractive methods. Extractive methods are preferable due to their simplicity compared with the more elaborate abstractive methods. In...
| Published in: | Language Resources & Evaluation Vol. 58; no. 4; pp. 1073 - 1092 |
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| Main Authors: | , , , |
| Format: | Article |
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Springer Nature
Dec2024
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| Subjects: | |
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=180627310&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 180627310 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Dec2024 vid: 58 iid: 4 pid: 237 pub: Springer Nature artinfo: ui: 180627310 10.1007/s10579-024-09739-7 ppf: 1073 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.1MB tig: atl: Features in extractive supervised single-document summarization: case of Persian news. aug: au: Rezaei, Hosein Mirhosseini, Seyed Amid Moeinzadeh Shahgholian, Azar Saraee, Mohamad affil: Isfahan University of Technology, Isfahan, Iran https://ror.org/04zfme737 Liverpool Business School, Liverpool John Moores University, Liverpool, UK https://ror.org/01tmqtf75 School of Science, Engineering and Environment, University of Salford, Manchester, UK su: Automatic summarization Natural language processing Text summarization Supervised learning Machine learning sug: subj: Automatic summarization Natural language processing Text summarization Supervised learning Machine learning keyword: Feature extraction Regression Supervised extractive summarization ab: Text summarization has been one of the most challenging areas of research in NLP. Much effort has been made to overcome this challenge by using either abstractive or extractive methods. Extractive methods are preferable due to their simplicity compared with the more elaborate abstractive methods. In extractive supervised single-document approaches, the system will not generate sentences. Instead, via supervised learning, it learns how to score sentences within the document based on some textual features and subsequently selects those with the highest rank. Therefore, the core objective is ranking, which enormously depends on the document structure and context. These dependencies have been unnoticed by many state-of-the-art solutions. In this work, document-related features such as topic and relative length are integrated into the vectors of every sentence to enhance the quality of summaries. Our experiment results show that the system takes contextual and structural patterns into account, which will increase the precision of the learned model. Consequently, our method will produce more comprehensive and concise summaries. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2024. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2024 holdings: @attributes: islocal: N |
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