Automatic Text Summarization Methods: A Comprehensive Review
Divakar Yadav, Jalpa Desai, Arun Kumar Yadav

TL;DR
This comprehensive review analyzes the state-of-the-art in automatic text summarization, covering approaches, datasets, evaluation metrics, challenges, and future research directions to address information overload.
Contribution
It provides an extensive overview of summarization techniques, evaluation methods, and identifies key challenges and opportunities for future research in the field.
Findings
Extractive and abstractive summarization are the most studied approaches.
Evaluation metrics and datasets are crucial for benchmarking progress.
Challenges include improving summary quality and developing reusable resources.
Abstract
One of the most pressing issues that have arisen due to the rapid growth of the Internet is known as information overloading. Simplifying the relevant information in the form of a summary will assist many people because the material on any topic is plentiful on the Internet. Manually summarising massive amounts of text is quite challenging for humans. So, it has increased the need for more complex and powerful summarizers. Researchers have been trying to improve approaches for creating summaries since the 1950s, such that the machine-generated summary matches the human-created summary. This study provides a detailed state-of-the-art analysis of text summarization concepts such as summarization approaches, techniques used, standard datasets, evaluation metrics and future scopes for research. The most commonly accepted approaches are extractive and abstractive, studied in detail in this…
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Taxonomy
TopicsTopic Modeling · Advanced Text Analysis Techniques · Text and Document Classification Technologies
