  Summarization is the task of compressing source document(s) into coherent and
succinct passages. This is a valuable tool to present users with concise and
accurate sketch of the top ranked documents related to their queries.
Query-based multi-document summarization (qMDS) addresses this pervasive need,
but the research is severely limited due to lack of training and evaluation
datasets as existing single-document and multi-document summarization datasets
are inadequate in form and scale. We propose a scalable approach called
AQuaMuSe to automatically mine qMDS examples from question answering datasets
and large document corpora. Our approach is unique in the sense that it can
general a dual dataset -- for extractive and abstractive summaries both. We
publicly release a specific instance of an AQuaMuSe dataset with 5,519
query-based summaries, each associated with an average of 6 input documents
selected from an index of 355M documents from Common Crawl. Extensive
evaluation of the dataset along with baseline summarization model experiments
are provided.
