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Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences

Journal of ICT Research and Applications (JICTRA) · Vol. 5 No. 1 (2013) · pp. 17–34

DOI
10.5614/itbj.ict.2011.5.1.2
ISSN
2338-5499, 2337-5787
Published
2013-09-13
Publisher
DRI - ITB
Language
eng

Abstract

This research employs free model that uses only sentential features without paragraph context to extract topic sentences of a paragraph. For finding optimal combination of features, corpus-based classification is used for constructing a sentence classifier as the model. The sentence classifier is trained by using Support Vector Machine (SVM). The experiment shows that position and meta-discourse features are more important than syntactic features to extract topic sentence, and the best performer (80.68%) is SVM classifier with all features.

Collaboration network of the authors

37 authors · 68 co-authorship links · 6 across different organisations

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  • Bandung Institute of Technology
  • Politeknik Penerbangan Surabaya
  • Indonesia University of Education
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Ayu PurwariantiBandung Institute of Technology2
Benhard SitohangBandung Institute of Technology2
Ghoziyah Haitan RachmanBandung Institute of Technology2
Muhammad Haris MaulanaBandung Institute of Technology1
Suhono Harso SupangkatBandung Institute of Technology1
Ahmad DahlanBandung Institute of Technology1
Tjong Wan SenBandung Institute of Technology1
Arry Akhmad ArmanBandung Institute of Technology1
Rila MandalaBandung Institute of Technology1
Budi RahardjoBandung Institute of Technology1
Muhammad Nur Kholish AbdurrazaqBandung Institute of Technology1
Muhammad Adi Khairul AnsharyBandung Institute of Technology1
Afrida HelenBandung Institute of Technology1
Jan Wira Gotama PutraBandung Institute of Technology1
Carmadi MachbubBandung Institute of Technology1
Wahyudi WahyudiBandung Institute of Technology1
Ary Setijadi PrihatmantoBandung Institute of Technology1
Saripudin SaripudinBandung Institute of Technology1
Modestus Oliver AsaliBandung Institute of Technology1
Toto IndriyantoBandung Institute of Technology1
Fariska Zakhralativa RuskandaBandung Institute of Technology1
Gusti Ayu Putri SaptawatiBandung Institute of Technology1
Nasy`an Taufiq Al GhifariBandung Institute of Technology1
Richard ReinhartBandung Institute of Technology1
Bariza HaqiBandung Institute of Technology1
Yudi Satria GondokaryonoBandung Institute of Technology1
Rina MardiatiBandung Institute of Technology1
Sony S WibowoAffiliation unknown1
Erwin Budi SetiawanBandung Institute of Technology1
Kridanto SurendroBandung Institute of Technology1
Lady Silk MoonlightPoliteknik Penerbangan Surabaya1
Bambang Bagus HariantoPoliteknik Penerbangan Surabaya1
Fiqqih FaizahPoliteknik Penerbangan Surabaya1

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://journals.itb.ac.id/index.php/jictra/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">Free Model of Sentence Classifier for Automatic Extraction of Topic Sentences</dc:title>
	<dc:creator>Khodra, M.L.</dc:creator>
	<dc:creator>Widyantoro, D.H.</dc:creator>
	<dc:creator>Aziz, E.A.</dc:creator>
	<dc:creator>Trilaksono, B.R.</dc:creator>
	<dc:description xml:lang="en-US">This  research  employs  free  model  that  uses  only  sentential  features without paragraph context  to extract topic sentences of a paragraph. For finding optimal  combination  of  features,  corpus-based  classification  is  used  for constructing a sentence classifier  as the model.  The sentence classifier is trained by  using Support Vector Machine  (SVM).  The experiment shows that position and meta-discourse features are more important  than syntactic features  to extract topic  sentence,  and  the  best  performer  (80.68%)  is  SVM  classifier  with  all features. </dc:description>
	<dc:publisher xml:lang="en-US">DRI - ITB</dc:publisher>
	<dc:date>2013-09-13</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US">Peer-reviewed Article</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journals.itb.ac.id/index.php/jictra/article/view/205</dc:identifier>
	<dc:identifier>10.5614/itbj.ict.2011.5.1.2</dc:identifier>
	<dc:source xml:lang="en-US">Journal of ICT Research and Applications; Vol. 5 No. 1 (2011); 17-34</dc:source>
	<dc:source>2338-5499</dc:source>
	<dc:source>2337-5787</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journals.itb.ac.id/index.php/jictra/article/view/205/199</dc:relation>
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