Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review
Given the rapid increase of textual data in various fields, text summarization has become essential for efficient information handling. Over recent decades, numerous methods have been proposed to enhance summarization processes, and various review papers and books have been published to encapsulate...
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| Format: | Article |
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2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10872906/ |
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| author | Maryam Azam Shah Khalid Sulaiman Almutairi Hasan Ali Khattak Abdallah Namoun Amjad Ali Hafiz Syed Muhammad Bilal |
| author_facet | Maryam Azam Shah Khalid Sulaiman Almutairi Hasan Ali Khattak Abdallah Namoun Amjad Ali Hafiz Syed Muhammad Bilal |
| author_sort | Maryam Azam |
| collection | DOAJ |
| description | Given the rapid increase of textual data in various fields, text summarization has become essential for efficient information handling. Over recent decades, numerous methods have been proposed to enhance summarization processes, and various review papers and books have been published to encapsulate these methodologies and discuss their implications. However, existing reviews often fail to provide a comprehensive retrospective of recent advancements, particularly concerning detailed architectural frameworks, the field’s current state, evaluation methodologies, and unresolved challenges. This paper addresses this gap by presenting a detailed analysis of the extractive approaches, encompassing their inherent strengths, limitations, and underlying mechanisms. We present a detailed, multi-layered architectural framework designed to advance and develop summarization models, thereby supporting researchers in their endeavors. The text summarization framework consists mainly of text preprocessing, feature extraction, sentence scoring, use of a base model, sentence selection and output summary, and post-processing. Furthermore, this review of 145 research articles categorizes domain-specific summarization techniques, focusing on unique challenges and tailored strategies for news, scientific articles, and social media. These techniques include statistical, fuzzy logic, rule, optimization, graph, clustering-based, machine learning, and deep learning. We emphasize the impact of evaluation metrics and benchmark datasets in performance assessment, providing a detailed analysis of the commonly utilized datasets and metrics (mainly ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-S) in the current literature. This review article is a valuable resource for advancing text summarization techniques in natural language processing and machine learning by identifying future research directions and open challenges. Notable challenges include expanding summarization for complex tasks, multiple documents, multimodal user input, multi-format and multilingual data, refining the stopping criteria, and improving the evaluation metrics. |
| format | Article |
| id | doaj-art-364b2ac9964a486a855017936e3ebf9e |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-364b2ac9964a486a855017936e3ebf9e2025-08-20T03:01:11ZengIEEEIEEE Access2169-35362025-01-0113281502816610.1109/ACCESS.2025.353888610872906Current Trends and Advances in Extractive Text Summarization: A Comprehensive ReviewMaryam Azam0https://orcid.org/0009-0002-5265-2695Shah Khalid1https://orcid.org/0000-0001-5735-5863Sulaiman Almutairi2https://orcid.org/0000-0003-4810-6018Hasan Ali Khattak3https://orcid.org/0000-0002-8198-9265Abdallah Namoun4https://orcid.org/0000-0002-7050-0532Amjad Ali5https://orcid.org/0000-0001-9117-3692Hafiz Syed Muhammad Bilal6School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, PakistanDepartment of Health Informatics, College of Public Health and Health Informatics, Qassim University, Buraydah, Qassim, Saudi ArabiaSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, PakistanAI Center, Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah, Saudi ArabiaDepartment of Computer and Software Technologies, University of Swat, Swat, Khyber Pakhtunkhwa, PakistanSchool of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, PakistanGiven the rapid increase of textual data in various fields, text summarization has become essential for efficient information handling. Over recent decades, numerous methods have been proposed to enhance summarization processes, and various review papers and books have been published to encapsulate these methodologies and discuss their implications. However, existing reviews often fail to provide a comprehensive retrospective of recent advancements, particularly concerning detailed architectural frameworks, the field’s current state, evaluation methodologies, and unresolved challenges. This paper addresses this gap by presenting a detailed analysis of the extractive approaches, encompassing their inherent strengths, limitations, and underlying mechanisms. We present a detailed, multi-layered architectural framework designed to advance and develop summarization models, thereby supporting researchers in their endeavors. The text summarization framework consists mainly of text preprocessing, feature extraction, sentence scoring, use of a base model, sentence selection and output summary, and post-processing. Furthermore, this review of 145 research articles categorizes domain-specific summarization techniques, focusing on unique challenges and tailored strategies for news, scientific articles, and social media. These techniques include statistical, fuzzy logic, rule, optimization, graph, clustering-based, machine learning, and deep learning. We emphasize the impact of evaluation metrics and benchmark datasets in performance assessment, providing a detailed analysis of the commonly utilized datasets and metrics (mainly ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-S) in the current literature. This review article is a valuable resource for advancing text summarization techniques in natural language processing and machine learning by identifying future research directions and open challenges. Notable challenges include expanding summarization for complex tasks, multiple documents, multimodal user input, multi-format and multilingual data, refining the stopping criteria, and improving the evaluation metrics.https://ieeexplore.ieee.org/document/10872906/Surveytext summarizationtransformer-based modelsdomain-specific summarizationgeneric architecturedatasets and evaluation measures |
| spellingShingle | Maryam Azam Shah Khalid Sulaiman Almutairi Hasan Ali Khattak Abdallah Namoun Amjad Ali Hafiz Syed Muhammad Bilal Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review IEEE Access Survey text summarization transformer-based models domain-specific summarization generic architecture datasets and evaluation measures |
| title | Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review |
| title_full | Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review |
| title_fullStr | Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review |
| title_full_unstemmed | Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review |
| title_short | Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review |
| title_sort | current trends and advances in extractive text summarization a comprehensive review |
| topic | Survey text summarization transformer-based models domain-specific summarization generic architecture datasets and evaluation measures |
| url | https://ieeexplore.ieee.org/document/10872906/ |
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