@InProceedings{dai-taneja-huang:2018:W18-43,
  author    = {Dai, Zeyu  and  Taneja, Himanshu  and  Huang, Ruihong},
  title     = {Fine-grained Structure-based News Genre Categorization},
  booktitle = {Proceedings of the Workshop Events and Stories in the News 2018},
  month     = {August},
  year      = {2018},
  address   = {Santa Fe, New Mexico, U.S.A},
  publisher = {Association for Computational Linguistics},
  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., {\it introducing the main story or event, catching the reader's attention} and {\it providing details}) in a news story and their writing style ({\it narrative} or {\it 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.},
  url       = {http://www.aclweb.org/anthology/W18-4308}
}