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All Journal Jurnal Informatika Polinema (JIP)
Hartawan, Gaduh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Library & Information Science

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Bidirectional and Auto-Regressive Transformer (BART) for Indonesian Abstractive Text Summarization Hartawan, Gaduh; Maylawati, Dian Sa'adillah; Uriawan, Wisnu
Jurnal Informatika Polinema Vol. 10 No. 4 (2024): Vol. 10 No. 4 (2024)
Publisher : UPT P2M State Polytechnic of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33795/jip.v10i4.5242

Abstract

Automatic summarization technology is developing rapidly to reduce reading time and obtain relevant information in Natural Language Processing technology research. There are two main approaches to text summarization: abstractive and extractive. The challenge of abstractive summarization results is higher than abstractive because abstractive summarization produces new and more natural words. Therefore, this research aims to produce abstractive summaries from Indonesian language texts with good readability. This research uses the Bidirectional and Auto-Regressive Transformer (BART) model, an innovative Transformers model combining two leading Transformer architectures, namely the BERT encoder and GPT decoder. The dataset used in this research is Liputan6, with model performance evaluation using ROUGE evaluation. The research results show that BART can produce good abstractive summaries with ROUGE-1, ROUGE-2, and ROUGE-L values of 37.19, 14.03, and 33.85, respectively.
Co-Authors Maylawati, Dian Sa'adillah Wisnu Uriawan, Wisnu