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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
Hanif Syahri Ramadhani
Universitas Sriwijaya
Computer Science & IT Engineering

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Abstractive Dialogue Summarization using Fine-Tuning Pre-Trained Language Model BART Desty Rodiah; Hanif Syahri Ramadhani
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 3 (2026): Juni 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i3.7488

Abstract

The increasing volume of conversational data on digital platforms necessitates effective automatic summarization methods. This study investigates the use of a pre-trained BART (Bidirectional and Auto-Regressive Transformers) model for abstractive dialogue summarization on the DialogSum dataset, which consists of 14,460 English dialogues. The model is fine-tuned using a sequence-to-sequence framework with systematic hyperparameter tuning, including variations in learning rate, beam size, and training epochs. The optimal configuration is achieved using a learning rate of 2e-5, a beam size of 6, and 5 training epochs. Model performance is evaluated using ROUGE and BERTScore metrics. The experimental results show that the proposed model attains an average ROUGE-L F1-score of 0.40, indicating moderate structural similarity between generated and reference summaries, and an average BERTScore F1-score of 0.69, reflecting strong semantic alignment. These findings suggest that fine-tuned BART is effective in preserving semantic relevance in abstractive dialogue summarization, although further improvements are required to enhance lexical precision and discourse-level coherence.
Co-Authors Desty Rodiah