Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring
The most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence and accuracy. This study’s improved summarization methodology overcomes these limitations by combinin...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10838534/ |
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| author | Estabraq Abdulreda Kadhim Mohammad-Reza Feizi-Derakhshi Hadi S. Aghdasi |
| author_facet | Estabraq Abdulreda Kadhim Mohammad-Reza Feizi-Derakhshi Hadi S. Aghdasi |
| author_sort | Estabraq Abdulreda Kadhim |
| collection | DOAJ |
| description | The most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence and accuracy. This study’s improved summarization methodology overcomes these limitations by combining the linguistic and semantic resources. Moreover, although it is more computationally complex, it efficiently combines higher quality with faster summarization. Specifically, a method relies on a weighted feature score scheme. For example, various textual features such as Named Entity Counts, Noun Counts, and Sentence Position contribute to the summarization quality appropriately. This study’s summarization algorithm was tested using the CNN, XSum and BBC Summarization datasets, which aggregate documents from different areas. The methodology was checked against traditional methods using ROUGE-1 and ROUGE-2, ROUGE-L and BERTScore. The last one, BERTScore, evaluates the semantic similarity of the generated summaries and the references. This study shows that the proposed methodology generates summaries that are not only informative but even semantically faithfully reproduce the original textual information; it achieves high scores in terms of F1-measure across different evaluations like BERTSCORE (0.8857) and ROUGE-1(0.6388), ROUGE-2(0.5662) and ROUGE-L (0.6421). It thus suggests that the approach is applicable in real-life situations and deserves further research. |
| format | Article |
| id | doaj-art-e29b4e39dbb0453a8e6ffe7ffc185b8d |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e29b4e39dbb0453a8e6ffe7ffc185b8d2025-01-31T00:02:01ZengIEEEIEEE Access2169-35362025-01-0113193021931910.1109/ACCESS.2025.352883010838534Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based ScoringEstabraq Abdulreda Kadhim0https://orcid.org/0000-0002-9657-2032Mohammad-Reza Feizi-Derakhshi1https://orcid.org/0000-0002-8548-976XHadi S. Aghdasi2https://orcid.org/0000-0003-1613-7370Department of Computer Engineering, Computerized Intelligence Systems Laboratory, University of Tabriz, Tabriz, IranDepartment of Computer Engineering, Computerized Intelligence Systems Laboratory, University of Tabriz, Tabriz, IranDepartment of Computer Engineering, University of Tabriz, Tabriz, IranThe most common traditional approaches to summarizing large texts while retaining their importance are TF-IDF and TextRank. However, these methods often fail to retain narrative coherence and accuracy. This study’s improved summarization methodology overcomes these limitations by combining the linguistic and semantic resources. Moreover, although it is more computationally complex, it efficiently combines higher quality with faster summarization. Specifically, a method relies on a weighted feature score scheme. For example, various textual features such as Named Entity Counts, Noun Counts, and Sentence Position contribute to the summarization quality appropriately. This study’s summarization algorithm was tested using the CNN, XSum and BBC Summarization datasets, which aggregate documents from different areas. The methodology was checked against traditional methods using ROUGE-1 and ROUGE-2, ROUGE-L and BERTScore. The last one, BERTScore, evaluates the semantic similarity of the generated summaries and the references. This study shows that the proposed methodology generates summaries that are not only informative but even semantically faithfully reproduce the original textual information; it achieves high scores in terms of F1-measure across different evaluations like BERTSCORE (0.8857) and ROUGE-1(0.6388), ROUGE-2(0.5662) and ROUGE-L (0.6421). It thus suggests that the approach is applicable in real-life situations and deserves further research.https://ieeexplore.ieee.org/document/10838534/Extractive text summarizationsentences scoringweighted featuresBBC-news datasetrouge metricBERTScore metric |
| spellingShingle | Estabraq Abdulreda Kadhim Mohammad-Reza Feizi-Derakhshi Hadi S. Aghdasi Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring IEEE Access Extractive text summarization sentences scoring weighted features BBC-news dataset rouge metric BERTScore metric |
| title | Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring |
| title_full | Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring |
| title_fullStr | Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring |
| title_full_unstemmed | Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring |
| title_short | Advanced Text Summarization Model Incorporating NLP Techniques and Feature-Based Scoring |
| title_sort | advanced text summarization model incorporating nlp techniques and feature based scoring |
| topic | Extractive text summarization sentences scoring weighted features BBC-news dataset rouge metric BERTScore metric |
| url | https://ieeexplore.ieee.org/document/10838534/ |
| work_keys_str_mv | AT estabraqabdulredakadhim advancedtextsummarizationmodelincorporatingnlptechniquesandfeaturebasedscoring AT mohammadrezafeiziderakhshi advancedtextsummarizationmodelincorporatingnlptechniquesandfeaturebasedscoring AT hadisaghdasi advancedtextsummarizationmodelincorporatingnlptechniquesandfeaturebasedscoring |