# Automatic Text Summarization Methods: A Comprehensive Review **Authors:** Divakar Yadav, Jalpa Desai, Arun Kumar Yadav **arXiv:** [2204.01849](https://arxiv.org/abs/2204.01849) **Date:** 2022-03-03 **Summary page:** https://www.summarizepaper.com/en/paper/2204.01849v1/ ## Key points - The rapid growth of the Internet has led to information overloading - Humans struggle to manually summarize large amounts of text - There is a demand for more complex and powerful summarizers - Extractive and abstractive methods are the two most commonly accepted approaches in text summarization - Evaluation metrics and methods for generated summaries are discussed - Challenges and research opportunities related to text summarization are highlighted - Summarization is the task of compressing a piece of text while retaining crucial information - Automatic summarization systems perform best with a compression rate between 15% to 30% - Various applications of automatic text summarization (ATS) systems are mentioned, including email, business reports, biographical extracts, legal documents, and books - Examples of ATS system applications include New York Times online news summaries, email summaries, and other advantages/pros ## Summary , , , , The rapid growth of the Internet has led to a significant issue known as information overloading. With an abundance of material available on any topic, it becomes challenging for humans to manually summarize massive amounts of text. This has increased the demand for more complex and powerful summarizers. Since the 1950s, researchers have been working on improving approaches for creating summaries that match those created by humans. This study provides a comprehensive analysis of text summarization concepts, including summarization approaches, techniques used, standard datasets, evaluation metrics, and future research opportunities. The two most commonly accepted approaches in text summarization are extractive and abstractive methods, which are studied in detail in this work. Evaluating the summary and developing reusable resources and infrastructure aid in comparing and replicating findings, fostering competition to enhance outcomes. The study also discusses different evaluation methods for generated summaries. In conclusion, this study highlights several challenges and research opportunities related to text summarization research that can be valuable for potential researchers in this field. The introduction section explains that summarization is the task of compressing a piece of text into a shorter version while retaining crucial informational aspects and content meaning. The compression rate τ is calculated by comparing the length of the summary to the length of the source document. Automatic summarization systems typically perform best with a compression rate between 15% to 30% of the source document's length. Additionally, the study mentions various applications of automatic text summarization (ATS) systems such as email and email thread summarization, report summarization for business professionals and researchers, biographical extracts, legal document summarization, and book summarization. The study also includes a research survey on ATS system applications with examples like New York Times online news summaries (5% to 10% of original text), email summaries (precision: 83%, recall: 85.7%), and other advantages/pros of ATS systems. Overall, this expanded summary provides a more detailed overview of the study's content, including the introduction to text summarization, applications of ATS systems, and specific examples from the research survey.