@inproceedings{du-cardie-2017-identifying,
    title = "Identifying Where to Focus in Reading Comprehension for Neural Question Generation",
    author = "Du, Xinya  and
      Cardie, Claire",
    editor = "Palmer, Martha  and
      Hwa, Rebecca  and
      Riedel, Sebastian",
    booktitle = "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
    month = sep,
    year = "2017",
    address = "Copenhagen, Denmark",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/D17-1219/",
    doi = "10.18653/v1/D17-1219",
    pages = "2067--2073",
    abstract = "A first step in the task of automatically generating questions for testing reading comprehension is to identify \textit{question-worthy} sentences, i.e. sentences in a text passage that humans find it worthwhile to ask questions about. We propose a hierarchical neural sentence-level sequence tagging model for this task, which existing approaches to question generation have ignored. The approach is fully data-driven {---} with no sophisticated NLP pipelines or any hand-crafted rules/features {---} and compares favorably to a number of baselines when evaluated on the SQuAD data set. When incorporated into an existing neural question generation system, the resulting end-to-end system achieves state-of-the-art performance for paragraph-level question generation for reading comprehension."
}