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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) · hlm. 451–459

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

Abstrak

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.

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40 penulis · 104 tautan kepenulisan bersama · 62 lintas organisasi · menampilkan 40 yang paling banyak terhubung

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  • Universitas Sumatera Utara
  • Telkom University
  • Politeknik Negeri Medan
  • Universitas Muhammadiyah Sumatera Utara
  • Universitas Labuhanbatu
  • International Islamic University Malaysia
  • Pertamina (Indonesia)
  • Organisasi lain
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  • Organisasi berbeda
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  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
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Rekan penulisOrganisasiArtikel bersama
Romi Fadillah RahmatUniversitas Sumatera Utara10
Mahyuddin K. M. NasutionUniversitas Sumatera Utara4
Julham JulhamPoliteknik Negeri Medan3
Idham KamilPoliteknik Negeri Medan3
Sarah PurnamawatiUniversitas Sumatera Utara3
Mhd Faris PratamaUniversitas Sumatera Utara3
Sharfina FazaPoliteknik Negeri Medan3
Syahril EfendiUniversitas Sumatera Utara2
Elviawaty Muisa ZamzamiUniversitas Sumatera Utara2
Opim Salim SitompulUniversitas Sumatera Utara2
Ferry FachrizalPoliteknik Negeri Medan2
Hariyadi HariyadiUniversitas Muhammadiyah Sumatera Barat2
Dini Oktarina Dwi HandayaniTaylor's University2
Tien Fabrianti KusumasariTelkom University1
Lukmanul HakimInternational Islamic University Malaysia1
Zahedi ZahediUniversitas Sumatera Utara1
Anjar AgusUniversitas Labuhanbatu1
Sarmin SuparniAfiliasi tidak diketahui1
Amin HarahapUniversitas Labuhanbatu1
Hanif FakhrurrojaTelkom University1
Rian Bimo AnkhalTelkom University1
Muhammad Fakhrul SafitraPertamina (Indonesia)1
Yulio FerdinandTelkom University1
Oktariani Nurul PratiwiTelkom University1
Sinung SuakantoTelkom University1
Jan M. PawlowskiAfiliasi tidak diketahui1
Litasari Widyastuti SuwarsonoTelkom University1
Joko SiswantoBandung Institute of Technology1
Syfa Nur LathifahTelkom University1
Amrullah AmrullahUniversitas Muhammadiyah Sumatera Utara1
Akbar IdamanUniversitas Satya Terra Bhinneka1
Herman MawengkangUniversitas Sumatera Utara1
Michael John WattsMedia Design School1
Anton Abdulbasah Kamilİstanbul Gelişim Üniversitesi1
Suherman SuhermanUniversitas Sumatera Utara1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) https://journal.yrpipku.com/index.php/jaets/oai, pertama di-harvest 9 Nov 2026, terakhir terlihat 9 Nov 2026.
Metadata asli (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>