GameWikiSum: a Novel Large Multi-Document Summarization Dataset
This addresses the problem of limited data for researchers in multi-document summarization, particularly for non-news domains, though it is incremental as it extends existing dataset creation methods to a new domain.
The authors tackled the lack of large multi-document summarization datasets by introducing GameWikiSum, a domain-specific dataset that is 100 times larger than existing ones and focuses on video game content, enabling training of both abstractive and extractive models.
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.