Multi-task Generative Dialogue Summarization Learning Framework with Topic-Based Data Augmentation

Generative Dialogue summarization involves extracting essential information from conversations and distilling it into succinct descriptions. This study introduces a novel multi-task learning framework that combines topic-based data augmentation and external knowledge generation for generative dialogue summarization. Our framework operates by annotating and enriching dialogue data based on topics at the data level, fostering varied expressions across different topics. Additionally, it integrates external knowledge at the entity and paragraph levels to help the model grasp common sense knowledge that is implicit in the context and enhance our model’s understanding of dialogue. Extensive experiments were carried out using two real-world datasets. The results demonstrate that our framework outperforms state-of-the-art models in dialogue summarization tasks, confirming the effectiveness of our proposed methodology.