  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.
