@InProceedings{subramanian-EtAl:2018:W18-26,
  author    = {Subramanian, Sandeep  and  Wang, Tong  and  Yuan, Xingdi  and  Zhang, Saizheng  and  Trischler, Adam  and  Bengio, Yoshua},
  title     = {Neural Models for Key Phrase Extraction and Question Generation},
  booktitle = {Proceedings of the Workshop on Machine Reading for Question Answering},
  month     = {July},
  year      = {2018},
  address   = {Melbourne, Australia},
  publisher = {Association for Computational Linguistics},
  pages     = {78--88},
  abstract  = {We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.},
  url       = {http://www.aclweb.org/anthology/W18-2609}
}