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SSAS: Semantic Similarity for Abstractive SummarizationAuthors: Raghuram Vadapalli,Litton J Kurisinkel,Manish Gupta,Vasudeva Varma Conference: 8th International Joint Conference on Natural Language Processing (IJCNLP-2017 2017) Location Taipei, Taiwan Date: 2017-11-27 Report no: IIIT/TR/2017/83 AbstractIdeally 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) 1 , 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, topical neutrality, paraphrasing, and optionally ROUGE score between a system generated and a human-written summary. Full paper: pdf Centre for Language Technologies Research Centre |
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