@InProceedings{caselli-vossen:2017:EventStory,
  author    = {Caselli, Tommaso  and  Vossen, Piek},
  title     = {The Event StoryLine Corpus: A New Benchmark for Causal and Temporal Relation Extraction},
  booktitle = {Proceedings of the Events and Stories in the News Workshop},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {77--86},
  abstract  = {This paper reports on the Event StoryLine Corpus (ESC) v1.0, a new benchmark
	dataset for the temporal and causal relation detection. By developing this
	dataset, we also introduce a new task, the StoryLine Extraction from news data,
	which aims at extracting and classifying events relevant for stories, from
	across news documents spread in time and clustered around a single seminal
	event or topic. In addition to describing the dataset, we also report on three
	baselines systems whose results show the complexity of the task and suggest
	directions for the development of more robust systems.},
  url       = {http://www.aclweb.org/anthology/W17-2711}
}