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Query-Focused Meeting Summarization (QFMS) aims to generate a summary of agiven meeting transcript conditioned upon a query. The main challenges for QFMSare the long input text length and sparse query-relevant information in themeeting transcript. In this paper, we propose a knowledge-enhanced two-stageframework called Knowledge-Aware Summarizer (KAS) to tackle the challenges. Inthe first stage, we introduce knowledge-aware scores to improve thequery-relevant segment extraction. In the second stage, we incorporatequery-relevant knowledge in the summary generation. Experimental results on theQMSum dataset show that our approach achieves state-of-the-art performance.Further analysis proves the competency of our methods in generating relevantand faithful summaries.

Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge