@InProceedings{eisenberg-finlayson:2017:EMNLP2017,
  author    = {Eisenberg, Joshua  and  Finlayson, Mark},
  title     = {A Simpler and More Generalizable Story Detector using Verb and Character Features},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  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.},
  url       = {https://www.aclweb.org/anthology/D17-1287}
}