  Today's research progress in the field of multi-document summarization is
obstructed by the small number of available datasets. Since the acquisition of
reference summaries is costly, existing datasets contain only hundreds of
samples at most, resulting in heavy reliance on hand-crafted features or
necessitating additional, manually annotated data. The lack of large corpora
therefore hinders the development of sophisticated models. Additionally, most
publicly available multi-document summarization corpora are in the news domain,
and no analogous dataset exists in the video game domain. In this paper, we
propose GameWikiSum, a new domain-specific dataset for multi-document
summarization, which is one hundred times larger than commonly used datasets,
and in another domain than news. Input documents consist of long professional
video game reviews as well as references of their gameplay sections in
Wikipedia pages. We analyze the proposed dataset and show that both abstractive
and extractive models can be trained on it. We release GameWikiSum for further
research: https://github.com/Diego999/GameWikiSum.
