@InProceedings{sanchan-aker-bontcheva:2017:NLPIR,
  author    = {Sanchan, Nattapong  and  Aker, Ahmet  and  Bontcheva, Kalina},
  title     = {Automatic Summarization of Online Debates},
  booktitle = {Proceedings of the 1st Workshop on Natural Language Processing and Information Retrieval associated with RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Inc.},
  pages     = {19--27},
  abstract  = {Debate summarization is one of the novel and challenging research areas in
	automatic text summarization which has been largely unexplored. In this paper,
	we develop a debate summarization pipeline to summarize key topics which are
	discussed or argued in the two opposing sides of online debates. We view that
	the generation of debate summaries can be achieved by clustering, cluster
	labeling, and visualization. In our work, we investigate two different
	clustering approaches for the generation of the summaries. In the first
	approach, we generate the summaries by applying purely term-based clustering
	and cluster labeling. The second approach makes use of X-means for clustering
	and Mutual Information for labeling the clusters. Both approaches are driven by
	ontologies.  We visualize the results using bar charts. We think that our
	results are a smooth entry for users aiming to receive the first impression
	about what is discussed within a debate topic containing waste number of
	argumentations.},
  url       = {https://doi.org/10.26615/978-954-452-038-0_003}
}