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Text summarization: BART, RF, and hybrid BART-RF algorithm comparison

IAES International Journal of Artificial Intelligence · Vol. 15 No. 1 (2026) · hlm. 929–940

DOI
10.11591/ijai.v15.i1.pp929-940
ISSN
2252-8938, 2089-4872
Terbit
2026-02-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Data and information accumulate quantitatively and qualitatively. Abundant text data are posted on the internet. The number correlates to the complexity of the summarization. Automatic text summarization (ATS) is one of the most challenging tasks in natural language processing (NLP). ATS approached in three ways: extractive, abstractive, and hybrid. Hybrid approach combines both extractive and abstractive. This research tests and compares performance of bidirectional auto-regressive transformer (BART) and random forest (RF) individually and the performance combination of hybrid BART and RF in ATS. The research shows that individually, BART and RF recall-oriented understudy for gisting evaluation (ROUGE) scores are having quite differences. Consecutively, ROUGE RF scores in R1, R2, and RL are 51.45, 45.52, and 54.58 respectively. Meanwhile, ROUGE BART scores are 32.78, 16.17, and 32.19. Consecutively, average ROUGE RF, BART, and RF×BART F-measure are 45.73, 21.38, and 31.31. RF has the highest average score. ATS hybrid RF×BART is shown to be performed better than the default BART. The average ROUGE F-measures for RF×BART obtain moderate score at 31.31. This score is better than the default BART’s ROUGE score. RF×BART can be an alternative to the effective hybrid approach.

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  • IPB University
  • Politeknik Negeri Nusa Utara
  • Universitas Halu Oleo
  • Universitas Kristen Wira Wacana Sumba
  • Pakuan University
  • Sekolah Tinggi Teknologi Wastukancana
  • Universitas Gadjah Mada
  • Afiliasi tidak diketahui
  • Organisasi berbeda
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Rekan penulisOrganisasiArtikel bersama
Karlisa PriandanaIPB University3
IrmansyahAfiliasi tidak diketahui1
Dhany Nugraha RamdhanyAfiliasi tidak diketahui1
Aziz KustiyoAfiliasi tidak diketahui1
Ekowati HandharyaniIPB University1
Mustakim MustakimUniversitas Halu Oleo1
Irman HermadiIPB University1
Arini Aha PekuwaliUniversitas Kristen Wira Wacana Sumba1
Wisnu Ananta KusumaIPB University1
Siti RaehanIPB University1
Medria Kusuma Dewi HardhienataAfiliasi tidak diketahui1
Sri NurdiatiIPB University1
Fajar Delli WihartikoPakuan University1
Edi SantosaIPB University1
Yeni HerdiyeniIPB University1
Iskandar Zulkarnaen SiregarIPB University1
Muhammad Rafi MuttaqinSekolah Tinggi Teknologi Wastukancana1
Stendy Budi Hartono SakurPoliteknik Negeri Nusa Utara1
Sony Hartono WijayaIPB University1
Adi Pandu Rahmat NababanPoliteknik Negeri Nusa Utara1
Miske SilangenPoliteknik Negeri Nusa Utara1
Cholid MawardiIPB University1
Herianto HeriantoUniversitas Gadjah Mada1
Sri WahjuniIPB University1
Hidayat HidayatIPB University1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Text summarization: BART, RF, and hybrid BART-RF algorithm comparison</dc:title>
	<dc:creator>Zamzam, Muhammad Adib</dc:creator>
	<dc:creator>Buono, Agus</dc:creator>
	<dc:creator>Haryanto, Toto</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Automatic text summarization; BART-RF; Hybrid; Random forest; ROUGE</dc:subject>
	<dc:description xml:lang="en-US">Data and information accumulate quantitatively and qualitatively. Abundant text data are posted on the internet. The number correlates to the complexity of the summarization. Automatic text summarization (ATS) is one of the most challenging tasks in natural language processing (NLP). ATS approached in three ways: extractive, abstractive, and hybrid. Hybrid approach combines both extractive and abstractive. This research tests and compares performance of bidirectional auto-regressive transformer (BART) and random forest (RF) individually and the performance combination of hybrid BART and RF in ATS. The research shows that individually, BART and RF recall-oriented understudy for gisting evaluation (ROUGE) scores are having quite differences. Consecutively, ROUGE RF scores in R1, R2, and RL are 51.45, 45.52, and 54.58 respectively. Meanwhile, ROUGE BART scores are 32.78, 16.17, and 32.19. Consecutively, average ROUGE RF, BART, and RF×BART F-measure are 45.73, 21.38, and 31.31. RF has the highest average score. ATS hybrid RF×BART is shown to be performed better than the default BART. The average ROUGE F-measures for RF×BART obtain moderate score at 31.31. This score is better than the default BART’s ROUGE score. RF×BART can be an alternative to the effective hybrid approach.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2026-02-01</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"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/24462</dc:identifier>
	<dc:identifier>10.11591/ijai.v15.i1.pp929-940</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 15, No 1: February 2026; 929-940</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v15.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/24462/14992</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2026 Muhammad Adib Zamzam, Agus Buono, Toto Haryanto</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>