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.

翻译：面向查询的会议摘要生成任务旨在根据给定查询，生成对应会议记录的摘要。该任务的主要挑战在于输入文本长度较长且会议记录中与查询相关的信息稀疏。本文提出一种知识增强的两阶段框架——知识感知摘要器，以应对上述挑战。第一阶段引入知识感知评分以改进查询相关片段提取；第二阶段将查询相关知识融入摘要生成过程。在QMSum数据集上的实验表明，本方法取得了当前最优性能。进一步分析证明，本方法能够生成相关且忠实于原文的摘要。