@InProceedings{vadapalli-EtAl:2017:I17-2,
  author    = {Vadapalli, Raghuram  and  J Kurisinkel, Litton  and  Gupta, Manish  and  Varma, Vasudeva},
  title     = {SSAS: Semantic Similarity for Abstractive Summarization},
  booktitle = {Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)},
  month     = {November},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {198--203},
  abstract  = {Ideally a metric evaluating an abstract system summary should represent the
	extent to which the system-generated summary approximates the semantic
	inference conceived by the reader using a human-written reference summary. Most
	of the previous approaches relied upon word or syntactic sub-sequence overlap
	to evaluate system-generated summaries. Such metrics cannot evaluate the
	summary at semantic inference level. Through this work we introduce the metric
	of Semantic Similarity for Abstractive Summarization (SSAS), which leverages
	natural language inference and paraphrasing techniques to frame a novel
	approach to evaluate system summaries at semantic inference level. SSAS is
	based upon a weighted composition of quantities representing the level of
	agreement, contradiction, independence, paraphrasing, and optionally ROUGE
	score between a system-generated and a human-written summary.},
  url       = {http://www.aclweb.org/anthology/I17-2034}
}