Let Topic Flow: A Unified Topic-Guided Segment-Wise Dialogue Summarization Framework
Qinyu Han, Zhihao Yang, Hongfei Lin, Tian Qin · IEEE/ACM Transactions on Audio Speech and Language Processing · 2024
Dialogue summarization is a task that aims to condense dialogues while retain the salient information. However, due to different domains involved in the dialogue, the corresponding format of reference summary varies from each other, e.g., QA pairs for customer service and SOAP notes in medical field. To address the common challenges encountered in various fields and alleviate the differences due to the format in the generation process, we introduce a novel unified topic-guided dialogue summarization framework, by which we can first capture the topic structure of the conversation and leverage it to guide the generation of the summary. This framework is the first to model fine-grained topic structure of the dialogue and pose its identification as a Seq2Seq task, as well as introduce the topic-guided segment-wise attention to produce the final summary in segments following the specific format in each domain. Such a concise but effective method avoids the trouble of customizing decoding schemes while retains the topic structure of a dialogue in its summary as much as possible. Comprehensive experiments were conducted on four benchmark datasets in different domains and the results show the better performance and generalization of our method compared with the baselines.