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

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	1819656X