@article{subramanian2017neural,
author = {Subramanian, Sandeep and Wang, Tong and Yuan, Xingdi and Zhang, Saizheng and Trischler, Adam and Bengio, Yoshua},
title = {Neural Models for Key Phrase Detection and Question Generation},
year = {2017},
month = {June},
abstract = {We propose a two-stage neural model to tackle question generation from documents. Our model first estimates the probability that word sequences in a document compose "interesting" answers using a neural model trained on a question-answering corpus. We thus take a data-driven approach to interestingness. Predicted key phrases then act as target answers that condition a sequence-to-sequence question generation model with a copy mechanism. Empirically, our neural key phrase detection model significantly outperforms an entity-tagging baseline system and existing rule-based approaches. We demonstrate that the question generator formulates good quality natural language questions from extracted key phrases, and a human study indicates that our system's generated question-answer pairs are competitive with those of an earlier approach. We foresee our system being used in an educational setting to assess reading comprehension and also as a data augmentation technique for semi-supervised learning.},
url = {http://approjects.co.za/?big=en-us/research/publication/neural-models-key-phrase-detection-question-generation/},
journal = {Neural and Evolutionary Computing},
}