Sentiment Summarization: Evaluating and Learning User Preferences

  title={Sentiment Summarization: Evaluating and Learning User Preferences},
  author={Kevin Lerman and Sasha Blair-Goldensohn and Ryan T. McDonald},
We present the results of a large-scale, end-to-end human evaluation of various sentiment summarization models. The evaluation shows that users have a strong preference for summarizers that model sentiment over non-sentiment baselines, but have no broad overall preference between any of the sentiment-based models. However, an analysis of the human judgments suggests that there are identifiable situations where one summarizer is generally preferred over the others. We exploit this fact to build… CONTINUE READING
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