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We propose a two-stage neural model to tackle question generation fromdocuments. First, our model estimates the probability that word sequences in adocument are ones that a human would pick when selecting candidate answers bytraining a neural key-phrase extractor on the answers in a question-answeringcorpus. Predicted key phrases then act as target answers and condition asequence-to-sequence question-generation model with a copy mechanism.Empirically, our key-phrase extraction model significantly outperforms anentity-tagging baseline and existing rule-based approaches. We furtherdemonstrate that our question generation system formulates fluent, answerablequestions from key phrases. This two-stage system could be used to augment orgenerate reading comprehension datasets, which may be leveraged to improvemachine reading systems or in educational settings.

Neural Models for Key Phrase Detection and Question Generation