Current Trends and Advances in Extractive Text Summarization: A Comprehensive Review

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. 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 extractive text summarization approaches, encompassing their inherent strengths, limitations, and underlying mechanisms. We present a detailed, multi-layered architectural framework designed to advance and develop summarization models. The text summarization framework consists of text preprocessing, feature extraction, sentence scoring, base model application, sentence selection, and post-processing. Furthermore, this review categorizes domain-specific summarization techniques covering statistical, fuzzy logic, rule-based, optimization, graph-based, clusteringbased, machine learning, and deep learning approaches. We emphasize evaluation metrics including ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-S on benchmark datasets.