Query-Utterance Attention with Joint modeling for Query-Focused Meeting Summarization
This work addresses the challenge of generating relevant summaries from long meeting transcripts in response to specific queries, which is an incremental improvement for natural language processing applications.
The paper tackles the problem of query-focused meeting summarization by proposing a query-aware framework that jointly models token and utterance relevance to the query, achieving new state-of-the-art performance on the QMSum dataset.
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.