A two-stage text summarization method based on an improved PEGASUS model and adaptive error correction mechanism.
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| Title: | A two-stage text summarization method based on an improved PEGASUS model and adaptive error correction mechanism. |
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| Authors: | ZHANG, Hang1, WU, Jun1 wujun@yzu.edu.cn |
| Source: | Computer Engineering & Science / Jisuanji Gongcheng yu Kexue. Feb2026, Vol. 48 Issue 2, p309-318. 10p. |
| Subjects: | Text summarization, Automatic summarization, Hierarchical clustering (Cluster analysis), Language models, Recurrent neural networks, Machine learning |
| Abstract: | To address the issues of word redundancy and poor readability in extractive summarization, as well as semantic confusion, logical inconsistency, and exposure bias in abstractive summarization, this paper proposes a two-stage text summarization method based on an improved PEGASUS model and an adaptive error correction mechanism, employing a hybrid summarization technique. In the extraction stage, text vectors are obtained using the BERT model, combined with a Bi-GRU and a graph structure. An improved MMR algorithm is utilized to effectively reduce redundancy in candidate summaries, enhancing summary precision. In the generation stage, the extracted sentences are processed by the PEGASUS model, incorporating hierarchical clustering technology and introducing an adaptive error correction mechanism to solve the out-of-vocabulary (OOV) problem. Additionally, a contrastive learning framework is adopted to significantly mitigate exposure bias. Experimental results demonstrate that the model established by our method achieves significant improvements in ROUGE scores on the NLPCC dataset, with average increases of 2.66 percentage points, 0.84 percentage points, and 1.81 percentage points across various metrics compared to models established by existing hybrid methods. This method not only improves summary quality but also exhibits superior performance in resolving OOV problem and exposure bias. [ABSTRACT FROM AUTHOR] |
| Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192370954 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A two-stage text summarization method based on an improved PEGASUS model and adaptive error correction mechanism. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22ZHANG%2C+Hang%22">ZHANG, Hang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22WU%2C+Jun%22">WU, Jun</searchLink><relatesTo>1</relatesTo><i> wujun@yzu.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Computer+Engineering+%26+Science+%2F+Jisuanji+Gongcheng+yu+Kexue%22">Computer Engineering & Science / Jisuanji Gongcheng yu Kexue</searchLink>. Feb2026, Vol. 48 Issue 2, p309-318. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Text+summarization%22">Text summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+summarization%22">Automatic summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Hierarchical+clustering+%28Cluster+analysis%29%22">Hierarchical clustering (Cluster analysis)</searchLink><br /><searchLink fieldCode="DE" term="%22Language+models%22">Language models</searchLink><br /><searchLink fieldCode="DE" term="%22Recurrent+neural+networks%22">Recurrent neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: To address the issues of word redundancy and poor readability in extractive summarization, as well as semantic confusion, logical inconsistency, and exposure bias in abstractive summarization, this paper proposes a two-stage text summarization method based on an improved PEGASUS model and an adaptive error correction mechanism, employing a hybrid summarization technique. In the extraction stage, text vectors are obtained using the BERT model, combined with a Bi-GRU and a graph structure. An improved MMR algorithm is utilized to effectively reduce redundancy in candidate summaries, enhancing summary precision. In the generation stage, the extracted sentences are processed by the PEGASUS model, incorporating hierarchical clustering technology and introducing an adaptive error correction mechanism to solve the out-of-vocabulary (OOV) problem. Additionally, a contrastive learning framework is adopted to significantly mitigate exposure bias. Experimental results demonstrate that the model established by our method achieves significant improvements in ROUGE scores on the NLPCC dataset, with average increases of 2.66 percentage points, 0.84 percentage points, and 1.81 percentage points across various metrics compared to models established by existing hybrid methods. This method not only improves summary quality but also exhibits superior performance in resolving OOV problem and exposure bias. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Computer Engineering & Science / Jisuanji Gongcheng yu Kexue is the property of Computer Engineering & Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3969/j.issn.1007-130X.2026.02.012 Languages: – Code: chi Text: Chinese PhysicalDescription: Pagination: PageCount: 10 StartPage: 309 Subjects: – SubjectFull: Text summarization Type: general – SubjectFull: Automatic summarization Type: general – SubjectFull: Hierarchical clustering (Cluster analysis) Type: general – SubjectFull: Language models Type: general – SubjectFull: Recurrent neural networks Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A two-stage text summarization method based on an improved PEGASUS model and adaptive error correction mechanism. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: ZHANG, Hang – PersonEntity: Name: NameFull: WU, Jun IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1007130X Numbering: – Type: volume Value: 48 – Type: issue Value: 2 Titles: – TitleFull: Computer Engineering & Science / Jisuanji Gongcheng yu Kexue Type: main |
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