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Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models

Journal of Applied Engineering and Technological Science (JAETS) · Vol. 5 No. 1 (2023) · pp. 451–459

DOI
10.37385/jaets.v5i1.2503
ISSN
2715-6079, 2715-6087
Published
2023-12-10
Publisher
Yayasan Riset dan Pengembangan Intelektual (YRPI)
Language
en

Abstract

In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.

Keywords

Collaboration network of the authors

40 authors · 102 co-authorship links · 4 across different organisations · showing the 40 most connected

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

  • Universitas Sumatera Utara
  • Politeknik Negeri Medan
  • Universitas Halu Oleo
  • International Islamic University Malaysia
  • Telkom University
  • Bandung Institute of Technology
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Romi Fadillah RahmatAffiliation unknown5
Arif Ridho LubisAffiliation unknown4
Romi Fadillah RahmatAffiliation unknown3
Michael J. WattsAffiliation unknown3
Julham JulhamAffiliation unknown3
Idham KamilAffiliation unknown3
Yuyun Yusnida LasePoliteknik Negeri Medan2
Syahril EfendiUniversitas Sumatera Utara2
Elviawaty Muisa ZamzamiUniversitas Sumatera Utara2
Opim Salim SitompulUniversitas Sumatera Utara2
Mahyuddin K. M. NasutionUniversitas Sumatera Utara2
Mahyuddin Khairuddin Matyuso NasutionAffiliation unknown2
Badaruddin BadaruddinUniversitas Halu Oleo1
Dewi WulandariAffiliation unknown1
Lilis Tiara AdhaPoliteknik Negeri Medan1
Tika AriyaniPoliteknik Negeri Medan1
Fahdi Saidi LubisInternational Islamic University Malaysia1
Hanif FakhrurrojaTelkom University1
Rian Bimo AnkhalAffiliation unknown1
Muhammad Fakhrul SafitraAffiliation unknown1
Yulio FerdinandAffiliation unknown1
Oktariani Nurul PratiwiAffiliation unknown1
Rio Guntur UtomoAffiliation unknown1
Jonathan Arya WibowoAffiliation unknown1
Kobthong LadkoomAffiliation unknown1
Sinung SuakantoBandung Institute of Technology1
Joko SiswantoAffiliation unknown1
Jan M. PawlowskiAffiliation unknown1
Syfa Nur LathifahAffiliation unknown1
Litasari Widyastuti SuwarsonoAffiliation unknown1
Hariyadi HariyadiAffiliation unknown1
Ferry FachrizalAffiliation unknown1
Amrullah AmrullahAffiliation unknown1
Akbar IdamanAffiliation unknown1
Herman MawengkangAffiliation unknown1

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.yrpipku.com/index.php/jaets/oai, first harvested 9 Nov 2026, last seen 9 Nov 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en">Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models</dc:title>
	<dc:creator xml:lang="en">Lubis, Arif Ridho</dc:creator>
	<dc:creator xml:lang="en">Safitri, Habibi Ramdani</dc:creator>
	<dc:creator xml:lang="en">Irvan, Irvan</dc:creator>
	<dc:creator xml:lang="en">Lubis, Muharman</dc:creator>
	<dc:creator xml:lang="en">Al-Khowarizmi, Al-Khowarizmi</dc:creator>
	<dc:subject xml:lang="en">Model T5</dc:subject>
	<dc:subject xml:lang="en">Seq2Seq Convolutional</dc:subject>
	<dc:subject xml:lang="en">ROUGE</dc:subject>
	<dc:description xml:lang="en">In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.</dc:description>
	<dc:publisher xml:lang="en">Yayasan Riset dan Pengembangan Intelektual (YRPI)</dc:publisher>
	<dc:date>2023-12-10</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.yrpipku.com/index.php/jaets/article/view/2503</dc:identifier>
	<dc:identifier>10.37385/jaets.v5i1.2503</dc:identifier>
	<dc:source xml:lang="en">Journal of Applied Engineering and Technological Science (JAETS); Vol. 5 No. 1 (2023): Journal of Applied Engineering and Technological Science (JAETS); 451-459</dc:source>
	<dc:source>2715-6079</dc:source>
	<dc:source>2715-6087</dc:source>
	<dc:source>10.37385/jaets.v5i1</dc:source>
	<dc:language>en</dc:language>
	<dc:relation>https://journal.yrpipku.com/index.php/jaets/article/view/2503/2124</dc:relation>
	<dc:rights xml:lang="en">Copyright (c) 2023 Journal of Applied Engineering and Technological Science (JAETS)</dc:rights>
</oai_dc:dc>