Improve Query Focused Abstractive Summarization by Incorporating Answer Relevance

Abstract

Query focused summarization (QFS) models aim to generate summaries from source documents that can answer the given query. Most previous work on QFS only considers the query relevance criterion when producing the summary. However, studying the effect of answer relevance in the summary generating process is also important. In this paper, we propose QFS-BART, a model that incorporates the explicit answer relevance of the source documents given the query via a question answering model, to generate coherent and answer-related summaries. Furthermore, our model can take advantage of large pre-trained models which improve the summarization performance significantly. Empirical results on the Debatepedia dataset show that the proposed model achieves the new state-of-the-art performance.

The two authors contribute equally. Accepted as a short paper in Findings of ACL 2021

References in corpus (8)

• MASS: Masked Sequence to Sequence Pre-training for Language Generation
• NewsQA: A Machine Comprehension Dataset
• Query Focused Abstractive Summarization: Incorporating Query Relevance, Multi-Document Coverage, and Summary Length Constraints into seq2seq Models
• CrossNER: Evaluating Cross-Domain Named Entity Recognition
• Conditional Self-Attention for Query-based Summarization
• AdaptSum: Towards Low-Resource Domain Adaptation for Abstractive Summarization
• Query Focused Multi-Document Summarization with Distant Supervision
• A Pilot Study of Domain Adaptation Effect for Neural Abstractive Summarization