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The Effect of Text Summarization in Essay Scoring (Case Study: Teach on E-Learning)

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 16 No. 1 (2022) · pp. 79–90

DOI
10.22146/ijccs.69906
ISSN
2460-7258, 1978-1520
Published
2022-01-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data.

Keywords

Collaboration network of the authors

13 authors · 18 co-authorship links · 2 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universitas Gadjah Mada
  • Universitas Teknokrat Indonesia
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Agus SihabuddinUniversitas Gadjah Mada2
Riszki Wijayatun PratiwiUniversitas Gadjah Mada2
Sigit PriyantaUniversitas Gadjah Mada1
Mhd. Theo Ari BangsaUniversitas Gadjah Mada1
Auliya Rahman IsnainUniversitas Teknokrat Indonesia1
Annisaa UtamiUniversitas Gadjah Mada1
Aina MusdholifahUniversitas Gadjah Mada1
Rayhan Naufal RamadhanUniversitas Gadjah Mada1
Fadhlan Pasyah Al FaridziUniversitas Gadjah Mada1
Fadila Shely AmaliaUniversitas Gadjah Mada1

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">The Effect of Text Summarization  in Essay Scoring (Case Study: Teach on E-Learning)</dc:title>
	<dc:creator>Syahra, Sensa Gudya Sauma</dc:creator>
	<dc:creator>Sari, Yunita</dc:creator>
	<dc:creator>Suyanto, Yohanes</dc:creator>
	<dc:subject xml:lang="en-US">Natural Language Processing</dc:subject>
	<dc:subject xml:lang="en-US">AES; ATS; readability index; unsupervised</dc:subject>
	<dc:description xml:lang="en-US">The development of automated essay scoring (AES) in the neural network (NN) approach has eliminated feature engineering. However, feature engineering is still needed, moreover, data with labels in the form of rubric scores, which are complementary to AES holistic scores, are still rarely found. In general, data without labels/scores is found more. However, unsupervised AES research has not progressed with the more common use of publicly labeled data. Based on the case studies adopted in the research, automatic text summarization (ATS) was used as a feature engineering model of AES and readability index as the definition of rubric values for data without labels.This research focuses on developing AES by implementing ATS results on SOM and HDBSCAN. The data used in this research are 403 documents of TEACH ON E-learning essays. Data is represented in the form of a combination of word vectors and a readability index. Based on the tests and measurements carried out, it was concluded that AES with ATS implementation had no good potential for the assessment of TEACH ON essays in increasing the silhouette score. The model produces the best silhouette score of 0.727286113 with original essay data.</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>2022-01-31</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:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/69906</dc:identifier>
	<dc:identifier>10.22146/ijccs.69906</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 16, No 1 (2022): January; 79-90</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/69906/33165</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2022 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>
</oai_dc:dc>