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

Yadav, Divakar; Katna, Rishbah; Yadav, Arun Kumar; Morato Lara, Jorge Luis

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

dc.affiliation.dpto	UC3M. Departamento de Informática	es
dc.affiliation.grupoinv	UC3M. Grupo de Investigación: GigaBD	es
dc.contributor.author	Yadav, Divakar
dc.contributor.author	Katna, Rishbah
dc.contributor.author	Yadav, Arun Kumar
dc.contributor.author	Morato Lara, Jorge Luis
dc.contributor.funder	---	es
dc.date.accessioned	2023-09-14T07:58:01Z
dc.date.available	2023-09-14T07:58:01Z
dc.date.issued	2022-12-20
dc.description.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.	en
dc.identifier.bibliographicCitation	D. Yadav, R. Katna, A. K. Yadav and J. Morato, "Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey," in IEEE Access, vol. 10, pp. 133981-134003, 2022, doi: 10.1109/ACCESS.2022.3231016.	en
dc.identifier.doi	https://doi.org/10.1109/ACCESS.2022.3231016
dc.identifier.publicationfirstpage	133981
dc.identifier.publicationlastpage	134003
dc.identifier.publicationtitle	IEEE Access	en
dc.identifier.publicationvolume	10
dc.identifier.uri	https://hdl.handle.net/10016/38325
dc.identifier.uxxi	AR/0000032443
dc.language.iso	eng	en
dc.publisher	IEEE	en
dc.rights	© The Authors, 2022	en
dc.rights	Atribución 3.0 España	*
dc.rights.accessRights	open access	en
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/es/	*
dc.subject.eciencia	Informática	es
dc.subject.other	abstractive summarization	en
dc.subject.other	cosin-similarity	en
dc.subject.other	deep learning	en
dc.subject.other	extractive summarization	en
dc.subject.other	graph-base algorith	en
dc.subject.other	neural networks	en
dc.title	Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey	en
dc.type	research article	*
dc.type.hasVersion	VoR	*
dspace.entity.type	Publication