Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge

• URL: http://arxiv.org/abs/2309.02105v1
• Date: Tue, 5 Sep 2023 10:26:02 GMT
• Title: Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge
• Authors: Tiezheng Yu, Ziwei Ji, Pascale Fung
• Abstract summary: 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.
• Score: 71.14873115781366
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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