@inproceedings{arumae-liu-2018-reinforced,
    title = "Reinforced Extractive Summarization with Question-Focused Rewards",
    author = "Arumae, Kristjan  and
      Liu, Fei",
    editor = "Shwartz, Vered  and
      Tabassum, Jeniya  and
      Voigt, Rob  and
      Che, Wanxiang  and
      de Marneffe, Marie-Catherine  and
      Nissim, Malvina",
    booktitle = "Proceedings of {ACL} 2018, Student Research Workshop",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/P18-3015/",
    doi = "10.18653/v1/P18-3015",
    pages = "105--111",
    abstract = "We investigate a new training paradigm for extractive summarization. Traditionally, human abstracts are used to derive goldstandard labels for extraction units. However, the labels are often inaccurate, because human abstracts and source documents cannot be easily aligned at the word level. In this paper we convert human abstracts to a set of Cloze-style comprehension questions. System summaries are encouraged to preserve salient source content useful for answering questions and share common words with the abstracts. We use reinforcement learning to explore the space of possible extractive summaries and introduce a question-focused reward function to promote concise, fluent, and informative summaries. Our experiments show that the proposed method is effective. It surpasses state-of-the-art systems on the standard summarization dataset."
}