Fine tuning the large language pegasus model for dialogue summarization

Dialogue summarization, a subset of task-oriented Natural Language Processing (NLP), faces challenges in providing concise and informative summaries of conversational data, which is crucial for various real-world applications. This study optimizes the PEGASUS model for abstractive dialogue summarization, a transformer architecture, on the SAMSum dataset. The research evaluates the model’s ability to produce understandable summaries using the ROUGE metric. The refined PEGASUS model demonstrates promising performance, evidenced by superior ROUGE scores. It effectively distils conversation essence into concise summaries, benefiting applications like information retrieval, chatbots, and conversational analysis. At the same time, our research showcases the effectiveness of optimizing the PEGASUS model for dialogue summarization; limitations exist, such as dataset specificity and model complexity. Future research should explore alternative model designs and fine-tuning methods on diverse datasets to improve summarization quality.