@inproceedings{dai-etal-2018-fine,
    title = "Fine-grained Structure-based News Genre Categorization",
    author = "Dai, Zeyu  and
      Taneja, Himanshu  and
      Huang, Ruihong",
    editor = "Caselli, Tommaso  and
      Miller, Ben  and
      van Erp, Marieke  and
      Vossen, Piek  and
      Palmer, Martha  and
      Hovy, Eduard  and
      Mitamura, Teruko  and
      Caswell, David  and
      Brown, Susan W.  and
      Bonial, Claire",
    booktitle = "Proceedings of the Workshop Events and Stories in the News 2018",
    month = aug,
    year = "2018",
    address = "Santa Fe, New Mexico, U.S.A",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/W18-4308/",
    pages = "61--67",
    abstract = "Journalists usually organize and present the contents of a news article following a well-defined structure. In this work, we propose a new task to categorize news articles based on their content presentation structures, which is beneficial for various NLP applications. We first define a small set of news elements considering their functions (e.g., \textit{introducing the main story or event, catching the reader{'}s attention} and \textit{providing details}) in a news story and their writing style (\textit{narrative} or \textit{expository}), and then formally define four commonly used news article structures based on their selections and organizations of news elements. We create an annotated dataset for structure-based news genre identification, and finally, we build a predictive model to assess the feasibility of this classification task using structure indicative features."
}