A Survey of Automatic Text Summarization: Progress, Process and Challenges

With the evolution of the Internet and multimedia technology, the amount of text data has increased exponentially. This text volume is a precious source of information and knowledge that needs to be efficiently summarized. Text summarization is the method to reduce the source text into a compact var...

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Autores principales:	M. F. Mridha, Aklima Akter Lima, Kamruddin Nur, Sujoy Chandra Das, Mahmud Hasan, Muhammad Mohsin Kabir
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Automatic text summarization feature extraction summarization methods performance measurement matrices challenges Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/54475f64f083464099ac60fe7319c66c
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id	oai:doaj.org-article:54475f64f083464099ac60fe7319c66c
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spelling	oai:doaj.org-article:54475f64f083464099ac60fe7319c66c2021-12-01T00:01:06ZA Survey of Automatic Text Summarization: Progress, Process and Challenges2169-353610.1109/ACCESS.2021.3129786https://doaj.org/article/54475f64f083464099ac60fe7319c66c2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9623462/https://doaj.org/toc/2169-3536With the evolution of the Internet and multimedia technology, the amount of text data has increased exponentially. This text volume is a precious source of information and knowledge that needs to be efficiently summarized. Text summarization is the method to reduce the source text into a compact variant, preserving its knowledge and the actual meaning. Here we thoroughly investigate the automatic text summarization (ATS) and summarize the widely recognized ATS architectures. This paper outlines extractive and abstractive text summarization technologies and provides a deep taxonomy of the ATS domain. The taxonomy presents the classical ATS algorithms to modern deep learning ATS architectures. Every modern text summarization approach’s workflow and significance are reviewed with the limitations with potential recovery methods, including the feature extraction approaches, datasets, performance measurement techniques, and challenges of the ATS domain, etc. In addition, this paper concisely presents the past, present, and future research directions in the ATS domain.M. F. MridhaAklima Akter LimaKamruddin NurSujoy Chandra DasMahmud HasanMuhammad Mohsin KabirIEEEarticleAutomatic text summarizationfeature extractionsummarization methodsperformance measurement matriceschallengesElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 156043-156070 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Automatic text summarization feature extraction summarization methods performance measurement matrices challenges Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Automatic text summarization feature extraction summarization methods performance measurement matrices challenges Electrical engineering. Electronics. Nuclear engineering TK1-9971 M. F. Mridha Aklima Akter Lima Kamruddin Nur Sujoy Chandra Das Mahmud Hasan Muhammad Mohsin Kabir A Survey of Automatic Text Summarization: Progress, Process and Challenges
description	With the evolution of the Internet and multimedia technology, the amount of text data has increased exponentially. This text volume is a precious source of information and knowledge that needs to be efficiently summarized. Text summarization is the method to reduce the source text into a compact variant, preserving its knowledge and the actual meaning. Here we thoroughly investigate the automatic text summarization (ATS) and summarize the widely recognized ATS architectures. This paper outlines extractive and abstractive text summarization technologies and provides a deep taxonomy of the ATS domain. The taxonomy presents the classical ATS algorithms to modern deep learning ATS architectures. Every modern text summarization approach’s workflow and significance are reviewed with the limitations with potential recovery methods, including the feature extraction approaches, datasets, performance measurement techniques, and challenges of the ATS domain, etc. In addition, this paper concisely presents the past, present, and future research directions in the ATS domain.
format	article
author	M. F. Mridha Aklima Akter Lima Kamruddin Nur Sujoy Chandra Das Mahmud Hasan Muhammad Mohsin Kabir
author_facet	M. F. Mridha Aklima Akter Lima Kamruddin Nur Sujoy Chandra Das Mahmud Hasan Muhammad Mohsin Kabir
author_sort	M. F. Mridha
title	A Survey of Automatic Text Summarization: Progress, Process and Challenges
title_short	A Survey of Automatic Text Summarization: Progress, Process and Challenges
title_full	A Survey of Automatic Text Summarization: Progress, Process and Challenges
title_fullStr	A Survey of Automatic Text Summarization: Progress, Process and Challenges
title_full_unstemmed	A Survey of Automatic Text Summarization: Progress, Process and Challenges
title_sort	survey of automatic text summarization: progress, process and challenges
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/54475f64f083464099ac60fe7319c66c
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A Survey of Automatic Text Summarization: Progress, Process and Challenges

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