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Automatic Summarization of English Text Based on the Fusion of Multiple Text Features.

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Title: Automatic Summarization of English Text Based on the Fusion of Multiple Text Features.
Authors: Wang, Yahui1 wangyh_lzjtu@163.com, Chang, Qingxia1 407503484@qq.com, Meng, Xuelei2 mengxl_lzjtu@163.com
Source: IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2540-2545. 6p.
Subjects: Automatic summarization, Data fusion (Statistics), Sentences (Grammar)
Abstract: Given that current automatic summarization models fail to comprehensively consider text features, this paper proposes a multi-feature fusion-based automatic text summarization model, namely Fusion of Multiple Text Features (FMTF). The model integrates seven features: similarity between sentences and the title, location information, key sentences, sentence length, clue words and transition words, keywords and proper nouns, and weighted summation. It employs weight coefficients for weighted calculation to form a novel automatic summarization model. Experimental results demonstrate that the proposed novel model achieves better performance in automatic summarization generation, with ROUGE scores outperforming those of currently common models. Overall, the model presented in this paper effectively enhances the accuracy of summary extraction, which is conducive to generating high-quality summaries. [ABSTRACT FROM AUTHOR]
Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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
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DbLabel: Engineering Source
An: 195088884
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Items – Name: Title
  Label: Title
  Group: Ti
  Data: Automatic Summarization of English Text Based on the Fusion of Multiple Text Features.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Yahui%22">Wang, Yahui</searchLink><relatesTo>1</relatesTo><i> wangyh_lzjtu@163.com</i><br /><searchLink fieldCode="AR" term="%22Chang%2C+Qingxia%22">Chang, Qingxia</searchLink><relatesTo>1</relatesTo><i> 407503484@qq.com</i><br /><searchLink fieldCode="AR" term="%22Meng%2C+Xuelei%22">Meng, Xuelei</searchLink><relatesTo>2</relatesTo><i> mengxl_lzjtu@163.com</i>
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  Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2540-2545. 6p.
– Name: Subject
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  Data: <searchLink fieldCode="DE" term="%22Automatic+summarization%22">Automatic summarization</searchLink><br /><searchLink fieldCode="DE" term="%22Data+fusion+%28Statistics%29%22">Data fusion (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Sentences+%28Grammar%29%22">Sentences (Grammar)</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Given that current automatic summarization models fail to comprehensively consider text features, this paper proposes a multi-feature fusion-based automatic text summarization model, namely Fusion of Multiple Text Features (FMTF). The model integrates seven features: similarity between sentences and the title, location information, key sentences, sentence length, clue words and transition words, keywords and proper nouns, and weighted summation. It employs weight coefficients for weighted calculation to form a novel automatic summarization model. Experimental results demonstrate that the proposed novel model achieves better performance in automatic summarization generation, with ROUGE scores outperforming those of currently common models. Overall, the model presented in this paper effectively enhances the accuracy of summary extraction, which is conducive to generating high-quality summaries. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) 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:
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 6
        StartPage: 2540
    Subjects:
      – SubjectFull: Automatic summarization
        Type: general
      – SubjectFull: Data fusion (Statistics)
        Type: general
      – SubjectFull: Sentences (Grammar)
        Type: general
    Titles:
      – TitleFull: Automatic Summarization of English Text Based on the Fusion of Multiple Text Features.
        Type: main
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            NameFull: Wang, Yahui
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            NameFull: Chang, Qingxia
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            NameFull: Meng, Xuelei
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          Dates:
            – D: 01
              M: 07
              Text: Jul2026
              Type: published
              Y: 2026
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              Value: 53
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              Value: 7
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            – TitleFull: IAENG International Journal of Computer Science
              Type: main
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