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.

本文提出了一种名为 AQuaMuSe 的可扩展方法，用于自动从问答数据集和大型文档语料库中挖掘基于查询的多文档摘要示例，其中包括种类独特的可提取和抽象式摘要数据集，并提供了详细的数据集评估和基线摘要模型实验。