@InProceedings{ajjour-EtAl:2017:ArgumentMining,
  author    = {Ajjour, Yamen  and  Chen, Wei-Fan  and  Kiesel, Johannes  and  Wachsmuth, Henning  and  Stein, Benno},
  title     = {Unit Segmentation of Argumentative Texts},
  booktitle = {Proceedings of the 4th Workshop on Argument Mining},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {118--128},
  abstract  = {The segmentation of an argumentative text into argument units and their
	non-argumentative counterparts is the first step in identifying the
	argumentative structure of the text. Despite its importance for argument
	mining, unit segmentation has been approached only sporadically so far. This
	paper studies the major parameters of unit segmentation systematically. We
	explore the effectiveness of various features, when capturing words separately,
	along with their neighbors, or even along with the entire text. Each such
	context is reflected by one machine learning model that we evaluate within and
	across three domains of texts. Among the models, our new deep learning approach
	capturing the entire text turns out best within all domains, with an F-score of
	up to 88.54. While structural features generalize best across domains, the
	domain transfer remains hard, which points to major challenges of unit
	segmentation.},
  url       = {http://www.aclweb.org/anthology/W17-5115}
}