Grasping Both Query Relevance and Essential Content for Query-focused Summarization

Numerous effective methods have been developed to improve query-focused summarization (QFS) performance, e.g., pre-trained model-based and query-answer relevance-based methods. However, these methods still suffer from missing or redundant information due to the inability to capture and effectively utilize the interrelationship between the query and the source document, as well as between the source document and its generated summary, resulting in the summary being unable to answer the query or containing additional unrequired information. To mitigate this problem, we propose an end-to-end hierarchical two-stage summarization model, that first predicts essential content, and then generates a summary by emphasizing the predicted important sentences while maintaining separate encodings for the query and the source, so that it can comprehend not only the query itself but also the essential information in the source. We evaluated the proposed model on two QFS datasets, and the results indicated its overall effectiveness and that of each component.