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Multi-document summarization (MDS) aims to compress the content in largedocument collections into short summaries and has important applications instory clustering for newsfeeds, presentation of search results, and timelinegeneration. However, there is a lack of datasets that realistically addresssuch use cases at a scale large enough for training supervised models for thistask. This work presents a new dataset for MDS that is large both in the totalnumber of document clusters and in the size of individual clusters. We buildthis dataset by leveraging the Wikipedia Current Events Portal (WCEP), whichprovides concise and neutral human-written summaries of news events, with linksto external source articles. We also automatically extend these source articlesby looking for related articles in the Common Crawl archive. We provide aquantitative analysis of the dataset and empirical results for severalstate-of-the-art MDS techniques.

A Large-Scale Multi-Document Summarization Dataset from the Wikipedia Current Events Portal