HiSum: Hierarchical Topic-Driven Approach for Role-Oriented Dialogue Summarisation

Abstract

As the volume of information on online communication platforms continues to grow, the task of dialogue summarisation becomes increasingly critical for understanding and extracting key information from diverse conversations. Traditional approaches often struggle to cope with the dynamic nature of dialogues, such as managing perspectives from multiple speakers and seamlessly transitioning between different topics. We propose a novel hierarchical topic-driven approach to generate role-oriented dialogue summarisation (HiSum) to address these challenges. First, we utilise VarGMM clustering technology for in-depth topic segmentation, which enables the model to capture the key topics in a dialogue. Second, we employ a LayerAttn hierarchical attention mechanism to dynamically adjust the focus of dialogue content based on participants' importance and the topics' relevance. Experimental results on three public dialogue summarisation data sets (CSDS, MC and SAMSUM) demonstrate that our method significantly outperforms most existing strong baseline methods across various evaluation metrics and surpasses the current state-of-the-art methods in certain metrics. Detailed analysis demonstrates that HiSum can perform more precise topic segmentation and effectively identify critical information. Our code is publicly available at: https://github.com/kjin0119/HiSum.