  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 AQuaMuSe,
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 ROUGE2, and 3.28 ROUGE-L when combined with transfer
learning strategies. Results from human evaluation suggest that the best models
produce more comprehensive and factually consistent summaries compared to a
baseline model. Code and checkpoints are made publicly available:
https://github.com/salesforce/query-focused-sum.
