A Novel Topic Segmentation Approach for Enhanced Dialogue Summarization

Dialogue summarization aims to distill a given conversation into a brief and focused summary. The challenge lies in the diverse perspectives of participants and the frequent shifts in topics throughout the dialogue. These factors make it difficult to extract and highlight the most significant information effectively. To tackle this challenge, we introduce a novel topic segmentation method that assigns distinct topics to dialogue segments while accounting for their importance and influence within the entire conversation. Our method's performance has been validated on two public benchmark datasets, CSDS and SAMSUM, demonstrating significant improvements in accuracy and coherence. The results show that our approach not only captures the essential content of dialogues more precisely but also enhances the overall quality and coverage of the summaries. This work provides a fresh approach to dialogue summarization and highlights its potential for practical application.