  Multi-document summarization (MDS) aims to compress the content in large
document collections into short summaries and has important applications in
story clustering for newsfeeds, presentation of search results, and timeline
generation. However, there is a lack of datasets that realistically address
such use cases at a scale large enough for training supervised models for this
task. This work presents a new dataset for MDS that is large both in the total
number of document clusters and in the size of individual clusters. We build
this dataset by leveraging the Wikipedia Current Events Portal (WCEP), which
provides concise and neutral human-written summaries of news events, with links
to external source articles. We also automatically extend these source articles
by looking for related articles in the Common Crawl archive. We provide a
quantitative analysis of the dataset and empirical results for several
state-of-the-art MDS techniques.
