Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge

Abstract

Meeting Transcripts Project Manager Query-Focused Meeting Summarization (QFMS) aims to generate a summary of a given meeting transcript conditioned upon a query. The main challenges for QFMS are the long input text length and sparse query-relevant information in the meeting transcript. In this paper, we propose a knowledge-enhanced two-stage framework called Knowledge-Aware Summarizer (KAS) to tackle the challenges. In the first stage, we introduce knowledge-aware scores to improve the query-relevant segment extraction. In the second stage, we incorporate query-relevant knowledge in the summary generation. Experimental results on the QMSum dataset show that our approach achieves state-of-the-art performance. Further analysis proves the competency of our methods in generating relevant and faithful summaries.1 1 Okay. Push-buttons are buttons not the most expensive , but do add extra cost . Industrial Designer Query What did the group think about the reduction of production costs ? Query-Relevant Knowledge Triple buttons add cost Figure 1: An example of query-relevant knowledge triple extracted from meeting transcripts. The knowledge triple can be used in query-relevant segment extraction as well as summary generation. particular query. Therefore, the models need to reduce the noisy information’s impact effectively. In this paper, we focus on the two-stage framework, extracting query-relevant segments from the meeting transcripts and then generating the summary based on the selected content. Compared to the end-to-end approaches (Zhu et al., 2020; Pagnoni et al., 2022) that directly encode the entire meeting transcripts, the two-stage framework is better at keeping computational efficiency and easier to scale up to longer inputs. Specifically, we propose Knowledge-Aware Summarizer (KAS) that incorporates query-relevant knowledge in both stages. In the first stage, we extract knowledge triples from the text segments and introduce knowledge-aware scores to improve segment ranking. In the second stage, the extracted query-relevant knowledge triples are utilized as extra input for the summary generation. We conduct experiments on a QFMS dataset named QMSum (Zhong et al., 2021) and achieve state-of-the-art performance. We further investigate how the different numbers of extracted segments affect the final performance. In addition, we manually evaluate the generation quality regarding fluency, relevance and factual correctness. Our contributions in this work are threefold: (1) Our work demonstrates the effectiveness of leveraging query-relevant knowledge in QFMS. (2) We