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In this paper, we introduce a new NLP task -- generating short factualarticles with references for queries by mining supporting evidence from theWeb. 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, weconstruct a large-scale dataset WebBrain-Raw by extracting English Wikipediaarticles and their crawlable Wikipedia references. WebBrain-Raw is ten timeslarger than the previous biggest peer dataset, which can greatly benefit theresearch community. From WebBrain-Raw, we construct two task-specific datasets:WebBrain-R and WebBrain-G, which are used to train in-domain retriever andgenerator, respectively. Besides, we empirically analyze the performances ofthe current state-of-the-art NLP techniques on WebBrain and introduce a newframework ReGen, which enhances the generation factualness by improved evidenceretrieval and task-specific pre-training for generation. Experiment resultsshow that ReGen outperforms all baselines in both automatic and humanevaluations.

WebBrain: Learning to Generate Factually Correct Articles for Queries by Grounding on Large Web Corpus