@InProceedings{stab-EtAl:2018:N18-5,
  author    = {Stab, Christian  and  Daxenberger, Johannes  and  Stahlhut, Chris  and  Miller, Tristan  and  Schiller, Benjamin  and  Tauchmann, Christopher  and  Eger, Steffen  and  Gurevych, Iryna},
  title     = {ArgumenText: Searching for Arguments in Heterogeneous Sources},
  booktitle = {Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Demonstrations},
  month     = {June},
  year      = {2018},
  address   = {New Orleans, Louisiana},
  publisher = {Association for Computational Linguistics},
  pages     = {21--25},
  abstract  = {Argument mining is a core technology for enabling argument search in large corpora. However, most current approaches fall short when applied to heterogeneous texts. In this paper, we present an argument retrieval system capable of retrieving sentential arguments for any given controversial topic. By analyzing the highest-ranked results extracted from Web sources, we found that our system covers 89% of arguments found in expert-curated lists of arguments from an online debate portal, and also identifies additional valid arguments.},
  url       = {http://www.aclweb.org/anthology/N18-5005}
}