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.
@article{arxiv.2309.02105,
title = {Improving Query-Focused Meeting Summarization with Query-Relevant Knowledge},
author = {Tiezheng Yu and Ziwei Ji and Pascale Fung},
journal= {arXiv preprint arXiv:2309.02105},
year = {2023}
}