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Text Summarization in Multi Document Using Genetic Algorithm

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 15 No. 4 (2021) · pp. 327–338

DOI
10.22146/ijccs.66026
ISSN
2460-7258, 1978-1520
Published
2021-10-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Automatic text summarization is a representation of a document that contains the essence or main focus of the document. Text summarization is automatically performed using the extraction method. The extraction method summarizes by copying the text that is considered the most important or most informative from the source text into a summary [1]. Documents can be divided into two types, namely single documents and multi documents. Multi document is input that comes from many documents from one or more sources that have more than one main idea.This study aims to summarize the text using a Genetic Algorithm by paying attention to the extraction of text features on each chromosome. The feature extraction used is sentence position, positive keywords, negative keywords, similarity between sentences, sentences containing entity words, sentences containing numbers, sentence length, connections between sentences, the number of connections between sentences. The number of chromosomes used is half of the number of public complaints. The data used is data on public complaints against the DIY government from February 2018 to July 2020. The data is obtained from the e-lapor DIY website. From the test results, the average value of Precision 1, Recall is 0.71, and f-measure value is 0.79.

Keywords

Collaboration network of the authors

6 authors · 6 co-authorship links · 2 across different organisations

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  • Universitas Gadjah Mada
  • Universitas Muhammadiyah Malang
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
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Co-authorOrganisationJoint articles
Silviani E RumagitUniversitas Gadjah Mada1
Bambang HermantoUniversitas Gadjah Mada1
Aminudin AminudinUniversitas Muhammadiyah Malang1
Baaras AhmadUniversitas Muhammadiyah Malang1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Text Summarization in Multi Document Using Genetic Algorithm</dc:title>
	<dc:creator>Hendrastuty, Nirwana</dc:creator>
	<dc:creator>SN, Azhari</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Automatic Text Sumarization, Feature Extraction , DIY government, Genetic Algorithm.</dc:subject>
	<dc:description xml:lang="en-US">Automatic text summarization is a representation of a document that contains the essence or main focus of the document. Text summarization is automatically performed using the extraction method. The extraction method summarizes by copying the text that is considered the most important or most informative from the source text into a summary [1]. Documents can be divided into two types, namely single documents and multi documents. Multi document is input that comes from many documents from one or more sources that have more than one main idea.This study aims to summarize the text using a Genetic Algorithm by paying attention to the extraction of text features on each chromosome. The feature extraction used is sentence position, positive keywords, negative keywords, similarity between sentences, sentences containing entity words, sentences containing numbers, sentence length, connections between sentences, the number of connections between sentences. The number of chromosomes used is half of the number of public complaints. The data used is data on public complaints against the DIY government from February 2018 to July 2020. The data is obtained from the e-lapor DIY website. From the test results, the average value of Precision 1, Recall is 0.71, and f-measure value is 0.79.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2021-10-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
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	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/66026</dc:identifier>
	<dc:identifier>10.22146/ijccs.66026</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 15, No 4 (2021): October; 327-338</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/66026/32403</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2021 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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