Query-focused meeting summarization(QFMS) aims to generate a specific summary for the given query according to the meeting transcripts. Due to the conflict between long meetings and limited input size, previous works mainly adopt extract-then-summarize methods, which use extractors to simulate binary labels or ROUGE scores to extract utterances related to the query and then generate a summary. However, the previous approach fails to fully use the comparison between utterances. To the extractor, comparison orders are more important than specific scores. In this paper, we propose a Ranker-Generator framework. It learns to rank the utterances by comparing them in pairs and learning from the global orders, then uses top utterances as the generator's input. We show that learning to rank utterances helps to select utterances related to the query effectively, and the summarizer can benefit from it. Experimental results on QMSum show that the proposed model outperforms all existing multi-stage models with fewer parameters.

翻译：查询导向的会议摘要（QFMS）旨在根据会议记录为给定查询生成特定摘要。由于长会议与有限输入长度之间的冲突，现有工作主要采用"先提取后摘要"的方法，即使用提取器模拟二元标签或ROUGE分数来提取与查询相关的话语，然后生成摘要。然而，现有方法未能充分利用话语间的比较信息。对于提取器而言，比较顺序比具体得分更为重要。本文提出排序器-生成器框架（Ranker-Generator framework），通过学习对话语进行两两比较并把握全局顺序来对话语排序，随后将排名靠前的话语作为生成器的输入。实验表明，话语排序学习能有效筛选出与查询相关的话语，并显著提升摘要生成质量。在QMSum数据集上的实验结果显示，所提模型以更少的参数优于所有现有多阶段模型。