In this paper, we introduce a new NLP task -- generating short factual
articles with references for queries by mining supporting evidence from the
Web. In this task, called WebBrain, the ultimate goal is to generate a fluent,
informative, and factually-correct short article (e.g., a Wikipedia article)
for a factual query unseen in Wikipedia. To enable experiments on WebBrain, we
construct a large-scale dataset WebBrain-Raw by extracting English Wikipedia
articles and their crawlable Wikipedia references. WebBrain-Raw is ten times
larger than the previous biggest peer dataset, which can greatly benefit the
research community. From WebBrain-Raw, we construct two task-specific datasets:
WebBrain-R and WebBrain-G, which are used to train in-domain retriever and
generator, respectively. Besides, we empirically analyze the performances of
the current state-of-the-art NLP techniques on WebBrain and introduce a new
framework ReGen, which enhances the generation factualness by improved evidence
retrieval and task-specific pre-training for generation. Experiment results
show that ReGen outperforms all baselines in both automatic and human
evaluations.

本文介绍了一个新的自然语言处理任务 —— 通过从 Web 中挖掘支持性证据，生成具有引用文献的短实证文章。为此，我们构建了一个大规模数据集，通过从英文维基百科文章及其可爬行的维基百科参考文献中提取而来。我们实验了当前最先进的 NLP 技术在 WebBrain 上的效果，并引入了一个新的框架 ReGen，它通过改进证据检索和任务特定的预训练来增强生成的实证性。实验结果表明，ReGen 在自动和人工评估中都优于所有基线。