@inproceedings{eisenberg-finlayson-2017-simpler,
    title = "A Simpler and More Generalizable Story Detector using Verb and Character Features",
    author = "Eisenberg, Joshua  and
      Finlayson, Mark",
    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/landing_page/D17-1287/",
    doi = "10.18653/v1/D17-1287",
    pages = "2708--2715",
    abstract = "Story detection is the task of determining whether or not a unit of text contains a story. Prior approaches achieved a maximum performance of 0.66 F1, and did not generalize well across different corpora. We present a new state-of-the-art detector that achieves a maximum performance of 0.75 F1 (a 14{\%} improvement), with significantly greater generalizability than previous work. In particular, our detector achieves performance above 0.70 F1 across a variety of combinations of lexically different corpora for training and testing, as well as dramatic improvements (up to 4,000{\%}) in performance when trained on a small, disfluent data set. The new detector uses two basic types of features{--}ones related to events, and ones related to characters{--}totaling 283 specific features overall; previous detectors used tens of thousands of features, and so this detector represents a significant simplification along with increased performance."
}