@InProceedings{wachsmuth-stein-ajjour:2017:EACLlong,
  author    = {Wachsmuth, Henning  and  Stein, Benno  and  Ajjour, Yamen},
  title     = {"PageRank" for Argument Relevance},
  booktitle = {Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers},
  month     = {April},
  year      = {2017},
  address   = {Valencia, Spain},
  publisher = {Association for Computational Linguistics},
  pages     = {1117--1127},
  abstract  = {Future search engines are expected to deliver pro and con arguments in response
	to queries on controversial topics. While argument mining is now in the focus
	of research, the question of how to retrieve the relevant arguments remains
	open. This paper proposes a radical model to assess relevance objectively at
	web scale: the relevance of an argument's conclusion is decided by what other
	arguments reuse it as a premise. We build an argument graph for this model that
	we analyze with a recursive weighting scheme, adapting key ideas of PageRank.
	In experiments on a large ground-truth argument graph, the resulting relevance
	scores correlate with human average judgments. We outline what natural language
	challenges must be faced at web scale in order to stepwise bring argument
	relevance to web search engines.},
  url       = {http://www.aclweb.org/anthology/E17-1105}
}