Automatic Text Summarization: A Review of Approaches, Challenges, and Future Directions - Zayed, Sara (2025)
Automatic text summarization (ATS) is a vital area in natural language processing focused on condensing lengthy documents into concise, meaningful summaries. Manual summarization remains time-consuming and costly, motivating extensive research into extractive, abstractive, and hybrid methods. This review provides a comprehensive survey of ATS, covering traditional techniques alongside recent breakthroughs fueled by machine learning, deep learning, and transformerbased architectures, including large language models (LLMs) such as GPT-4, Claude, Falcon, and MPT. We introduce a novel multi-dimensional taxonomy that integrates classical methods, emerging paradigms, domain-specific and multimodal approaches, and instruction-tuned LLMs. Our survey analyzes the strengths and limitations of each approach and discusses evaluation methodologies, incorporating both established metrics like ROUGE and BLEU and newer ones. We further address key challenges including factual consistency, prompt sensitivity, explainability, ethical considerations, and computational efficiency. By bridging traditional summarization techniques with cutting-edge LLMbased models, this paper highlights current limitations, emerging opportunities, and future directions aimed at developing robust, reliable, and scalable summarization systems for diverse realworld applications.