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Current trends and advances in extractive text summarization : A comprehensive review
Azam, Maryam Khalid, Shah Almutairi, Sulaiman Khattak, Hasan Ali Namoun, Abdallah Ali, Amjad Syed Muhammad Bilal, Hafiz
Azam, Maryam
Khalid, Shah
Almutairi, Sulaiman
Khattak, Hasan Ali
Namoun, Abdallah
Ali, Amjad
Syed Muhammad Bilal, Hafiz
Abstract
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.
Keywords
survey, text summarization, transformer-based models, domain-specific summarization, generic architecture, datasets and evaluation measures
Date
2025
Type
Journal article
Journal
IEEE Access
Book
Volume
13
Issue
Page Range
28150-28166
Article Number
ACU Department
Peter Faber Business School
Faculty of Law and Business
Faculty of Law and Business
Collections
Relation URI
Source URL
Event URL
Open Access Status
Published as ‘gold’ (paid) open access
License
CC BY 4.0
File Access
Open
Notes
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.