Query-focused meeting summarization (QFMS) aims to generate summaries from meeting transcripts in response to a given query. Previous works typically concatenate the query with meeting transcripts and implicitly model the query relevance only at the token level with attention mechanism. However, due to the dilution of key query-relevant information caused by long meeting transcripts, the original transformer-based model is insufficient to highlight the key parts related to the query. In this paper, we propose a query-aware framework with joint modeling token and utterance based on Query-Utterance Attention. It calculates the utterance-level relevance to the query with a dense retrieval module. Then both token-level query relevance and utterance-level query relevance are combined and incorporated into the generation process with attention mechanism explicitly. We show that the query relevance of different granularities contributes to generating a summary more related to the query. Experimental results on the QMSum dataset show that the proposed model achieves new state-of-the-art performance.

翻译：面向查询的会议摘要（QFMS）旨在根据给定查询从会议记录中生成摘要。以往的工作通常将查询与会议记录拼接，并仅在令牌层面通过注意力机制隐式建模查询相关性。然而，由于长会议记录导致关键查询相关信息的稀释，原始的基于Transformer的模型不足以突出与查询相关的关键部分。本文提出了一种基于查询-话语注意力的查询感知框架，该框架联合建模令牌和话语级别。它通过密集检索模块计算话语级与查询的相关性。随后，令牌级查询相关性与话语级查询相关性被结合，并通过注意力机制显式融入生成过程。我们表明，不同粒度的查询相关性有助于生成与查询更相关的摘要。在QMSum数据集上的实验结果显示，所提出的模型达到了新的最优性能。