Automatic Text Summarization: A Review of Approaches, Challenges, and Future Directions

Automatic text summarization (ATS) is a critical area in natural language processing that aims to condense long documents into concise and meaningful summaries. Manual text summarization is a time-consuming, labor-intensive, and costly process. Researchers have been actively working to enhance ATS techniques, focusing on either extractive, abstractive, or hybrid methods. This review explores different summarization techniques, including extractive and abstract approaches, with a focus on recent developments using machine learning and deep learning models. The paper also discusses the challenges in generating high-quality summaries, including coherence, redundancy, and information loss. Finally, this review provides a comprehensive survey for researchers, presenting the various aspects of ATS, including approaches, taxonomy analysis, datasets, evaluation methods, and future research trends.