Improved Text Summarization of News Articles Using GA-HC and PSO-HC

Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarizati...

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Autores principales:	Muhammad Mohsin, Shazad Latif, Muhammad Haneef, Usman Tariq, Muhammad Attique Khan, Sefedine Kadry, Hwan-Seung Yong, Jung-In Choi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	Automatic Text Summarization (ATS) genetic algorithm Hierarchical Clustering Technique (HCT) agglomerative clustering extracted summary Single Document Summarization Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/596e1551183749ed824cc5297043dd9f
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spelling	oai:doaj.org-article:596e1551183749ed824cc5297043dd9f2021-11-25T16:30:09ZImproved Text Summarization of News Articles Using GA-HC and PSO-HC10.3390/app1122105112076-3417https://doaj.org/article/596e1551183749ed824cc5297043dd9f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10511https://doaj.org/toc/2076-3417Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.Muhammad MohsinShazad LatifMuhammad HaneefUsman TariqMuhammad Attique KhanSefedine KadryHwan-Seung YongJung-In ChoiMDPI AGarticleAutomatic Text Summarization (ATS)genetic algorithmHierarchical Clustering Technique (HCT)agglomerative clusteringextracted summarySingle Document SummarizationTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10511, p 10511 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Automatic Text Summarization (ATS) genetic algorithm Hierarchical Clustering Technique (HCT) agglomerative clustering extracted summary Single Document Summarization Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	Automatic Text Summarization (ATS) genetic algorithm Hierarchical Clustering Technique (HCT) agglomerative clustering extracted summary Single Document Summarization Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Muhammad Mohsin Shazad Latif Muhammad Haneef Usman Tariq Muhammad Attique Khan Sefedine Kadry Hwan-Seung Yong Jung-In Choi Improved Text Summarization of News Articles Using GA-HC and PSO-HC
description	Automatic Text Summarization (ATS) is gaining attention because a large volume of data is being generated at an exponential rate. Due to easy internet availability globally, a large amount of data is being generated from social networking websites, news websites and blog websites. Manual summarization is time consuming, and it is difficult to read and summarize a large amount of content. Automatic text summarization is the solution to deal with this problem. This study proposed two automatic text summarization models which are Genetic Algorithm with Hierarchical Clustering (GA-HC) and Particle Swarm Optimization with Hierarchical Clustering (PSO-HC). The proposed models use a word embedding model with Hierarchal Clustering Algorithm to group sentences conveying almost same meaning. Modified GA and adaptive PSO based sentence ranking models are proposed for text summary in news text documents. Simulations are conducted and compared with other understudied algorithms to evaluate the performance of proposed methodology. Simulations results validate the superior performance of the proposed methodology.
format	article
author	Muhammad Mohsin Shazad Latif Muhammad Haneef Usman Tariq Muhammad Attique Khan Sefedine Kadry Hwan-Seung Yong Jung-In Choi
author_facet	Muhammad Mohsin Shazad Latif Muhammad Haneef Usman Tariq Muhammad Attique Khan Sefedine Kadry Hwan-Seung Yong Jung-In Choi
author_sort	Muhammad Mohsin
title	Improved Text Summarization of News Articles Using GA-HC and PSO-HC
title_short	Improved Text Summarization of News Articles Using GA-HC and PSO-HC
title_full	Improved Text Summarization of News Articles Using GA-HC and PSO-HC
title_fullStr	Improved Text Summarization of News Articles Using GA-HC and PSO-HC
title_full_unstemmed	Improved Text Summarization of News Articles Using GA-HC and PSO-HC
title_sort	improved text summarization of news articles using ga-hc and pso-hc
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/596e1551183749ed824cc5297043dd9f
work_keys_str_mv	AT muhammadmohsin improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT shazadlatif improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT muhammadhaneef improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT usmantariq improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT muhammadattiquekhan improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT sefedinekadry improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT hwanseungyong improvedtextsummarizationofnewsarticlesusinggahcandpsohc AT junginchoi improvedtextsummarizationofnewsarticlesusinggahcandpsohc
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Improved Text Summarization of News Articles Using GA-HC and PSO-HC

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