Abstractive Dialogue Summarization using Fine-Tuning Pre-Trained Language Model BART - Desty Rodiah; Hanif Syahri Ramadhani (Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), 2026)
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.