Cover Image

A review of graph-based extractive text summarization models

The amount of text data is continuously increasing both at online and offline storage, that makes is difficult for people to read across and find the desired information within a possible available time. This necessitate the use of technique such as automatic text summarization. A text summary is th...

Full description

Saved in:
Bibliographic Details
Main Authors: Abubakar Bichi, Abdulkadir, Samsudin, Ruhaidah, Hassan, Rohayanti, Almekhlafi, Khalil
Format: Article
Published: Springer Science and Business Media Deutschland GmbH 2021
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://eprints.utm.my/id/eprint/100257/
http://dx.doi.org/10.1007/978-3-030-70713-2_41
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.utm.100257
record_format eprints
spelling my.utm.1002572023-03-29T07:06:21Z http://eprints.utm.my/id/eprint/100257/ A review of graph-based extractive text summarization models Abubakar Bichi, Abdulkadir Samsudin, Ruhaidah Hassan, Rohayanti Almekhlafi, Khalil QA75 Electronic computers. Computer science The amount of text data is continuously increasing both at online and offline storage, that makes is difficult for people to read across and find the desired information within a possible available time. This necessitate the use of technique such as automatic text summarization. A text summary is the briefer form of the original text, in which the principal document message is preserved. Many approaches and algorithms have been proposed for automatic text summarization including, supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a review of various graph-based automatic text summarization models. Springer Science and Business Media Deutschland GmbH 2021 Article PeerReviewed Abubakar Bichi, Abdulkadir and Samsudin, Ruhaidah and Hassan, Rohayanti and Almekhlafi, Khalil (2021) A review of graph-based extractive text summarization models. Lecture Notes on Data Engineering and Communications Technologies, 72 (NA). pp. 439-448. ISSN 2367-4512 http://dx.doi.org/10.1007/978-3-030-70713-2_41 DOI : 10.1007/978-3-030-70713-2_41
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Abubakar Bichi, Abdulkadir
Samsudin, Ruhaidah
Hassan, Rohayanti
Almekhlafi, Khalil
A review of graph-based extractive text summarization models
description The amount of text data is continuously increasing both at online and offline storage, that makes is difficult for people to read across and find the desired information within a possible available time. This necessitate the use of technique such as automatic text summarization. A text summary is the briefer form of the original text, in which the principal document message is preserved. Many approaches and algorithms have been proposed for automatic text summarization including, supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a review of various graph-based automatic text summarization models.
format Article
author Abubakar Bichi, Abdulkadir
Samsudin, Ruhaidah
Hassan, Rohayanti
Almekhlafi, Khalil
author_facet Abubakar Bichi, Abdulkadir
Samsudin, Ruhaidah
Hassan, Rohayanti
Almekhlafi, Khalil
author_sort Abubakar Bichi, Abdulkadir
title A review of graph-based extractive text summarization models
title_short A review of graph-based extractive text summarization models
title_full A review of graph-based extractive text summarization models
title_fullStr A review of graph-based extractive text summarization models
title_full_unstemmed A review of graph-based extractive text summarization models
title_sort review of graph-based extractive text summarization models
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2021
url http://eprints.utm.my/id/eprint/100257/
http://dx.doi.org/10.1007/978-3-030-70713-2_41
_version_ 1762392180940341248
score 13.209306

Similar Items