论文标题
GameWikisum：一种新颖的大型多文件摘要数据集
GameWikiSum: a Novel Large Multi-Document Summarization Dataset
论文作者
论文摘要
当今的多文章摘要领域的研究进度被少数可用数据集所阻碍。由于获取参考摘要是昂贵的，因此现有的数据集最多只包含数百个样本，从而极大地依赖手工制作的功能或需要其他手动注释的数据。因此，缺乏大型语料库阻碍了复杂模型的发展。此外，大多数公开可用的多文件摘要语料库都在新闻领域中，并且在视频游戏域中不存在类似的数据集。在本文中，我们提出了GameWikisum，这是一种用于多文档摘要的新域特异性数据集，该数据集比常用数据集大的一百倍，并且在其他域中比新闻更大。输入文档包括长期的专业视频游戏评论以及Wikipedia页面中其游戏节目的参考。我们分析了提出的数据集，并表明可以在其上培训抽象和提取模型。我们发布GameWikisum进行进一步研究：https：//github.com/diego999/gamewikisum。
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.
