Exploring Neural Models for Query-Focused Summarization
Jesse Vig, Alexander R. Fabbri, Wojciech Kryściński, Chien-Sheng Wu, Wenhao Liu
Abstract
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released datasets, such as QMSum or AQua-MuSe, facilitate research efforts in QFS, the field lacks a comprehensive study of the broad space of applicable modeling methods. In this paper we conduct a systematic exploration of neural approaches to QFS, considering two general classes of methods: two-stage extractive-abstractive solutions and end-to-end models. Within those categories, we investigate existing models and explore strategies for transfer learning. We also present two modeling extensions that achieve state-of-the-art performance on the QMSum dataset, up to a margin of 3.38 ROUGE-1, 3.72 ROUGE-2, and 3.28 ROUGE-L when combined with transfer learning strategies. Results from human evaluation suggest that the best models produce more comprehensive and factuallyconsistent summaries compared to a baseline model. Code and checkpoints are made publicly available: https://github.com/ salesforce/query-focused-sum.