Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey

With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a sig...

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Published in:	IEEE access Vol. 10; p. 1
Main Authors:	Yadav, Divakar, Katna, Rishabh, Yadav, Arun Kumar, Morato, Jorge
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.01.2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Abstractive summarization Computer science cosine-similarity Data mining Deep learning Documents extractive summarization graph-based algorithm Measurement Natural language processing Neural networks Social networks Web sites
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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Abstract	With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a significant area of research in natural language processing (NLP). With the expansion of the internet, the amount of data in the world has exploded. Large volumes of data make locating the required and best information time-consuming. It is impractical to manually summarize petabytes of data; hence, computerized text summarization is rising in popularity. This study presents a comprehensive overview of the current status of text summarizing approaches, techniques, standard datasets, assessment criteria, and future research directions. The summarizing approaches are assessed based on several characteristics, including approach-based, document-number-based, Summarization domain-based, document-language-based, output summary nature, etc. This study concludes with a discussion of many obstacles and research opportunities linked to text summarizing research that may be relevant for future researchers in this field.
AbstractList	With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a significant area of research in natural language processing (NLP). With the expansion of the internet, the amount of data in the world has exploded. Large volumes of data make locating the required and best information time-consuming. It is impractical to manually summarize petabytes of data; hence, computerized text summarization is rising in popularity. This study presents a comprehensive overview of the current status of text summarizing approaches, techniques, standard datasets, assessment criteria, and future research directions. The summarizing approaches are assessed based on several characteristics, including approach-based, document-number-based, Summarization domain-based, document-language-based, output summary nature, etc. This study concludes with a discussion of many obstacles and research opportunities linked to text summarizing research that may be relevant for future researchers in this field.
Author	Yadav, Arun Kumar Katna, Rishabh Yadav, Divakar Morato, Jorge
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SubjectTerms	Abstractive summarization Computer science cosine-similarity Data mining Deep learning Documents extractive summarization graph-based algorithm Measurement Natural language processing Neural networks Social networks Web sites
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Title	Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey
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