Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge
This work addresses the problem of generating query-specific summaries from meeting transcripts for users needing targeted information extraction, representing an incremental improvement over existing methods.
The paper tackles the challenges of long input text and sparse query-relevant information in Query-Focused Meeting Summarization (QFMS) by proposing a knowledge-enhanced two-stage framework called Knowledge-Aware Summarizer (KAS), achieving state-of-the-art performance on the QMSum dataset.
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.