  The progress in Query-focused Multi-Document Summarization (QMDS) has been
limited by the lack of sufficient largescale high-quality training datasets. We
present two QMDS training datasets, which we construct using two data
augmentation methods: (1) transferring the commonly used single-document
CNN/Daily Mail summarization dataset to create the QMDSCNN dataset, and (2)
mining search-query logs to create the QMDSIR dataset. These two datasets have
complementary properties, i.e., QMDSCNN has real summaries but queries are
simulated, while QMDSIR has real queries but simulated summaries. To cover both
these real summary and query aspects, we build abstractive end-to-end neural
network models on the combined datasets that yield new state-of-the-art
transfer results on DUC datasets. We also introduce new hierarchical encoders
that enable a more efficient encoding of the query together with multiple
documents. Empirical results demonstrate that our data augmentation and
encoding methods outperform baseline models on automatic metrics, as well as on
human evaluations along multiple attributes.
