Automatic Text Summarization (ATS) is a crucial task in Natural Language Processing (NLP), seeking to distill critical information from voluminous textual data. This review provides a concise overview of extractive and abstractive summarization techniques. We explore the methodology used in various papers and extractive features usually implemented in building models like text summarization. Literature review, process, and comparative results are discussed, offering analysis of prominent approaches. This review is a quick reference for researchers and practitioners navigating the evolving landscape of auto-text summarization.
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Gohad et al. (2024) studied this question.
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