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 summar...
Kevin Lerman, Sasha Blair-Goldensohn, Ryan T. McDo...
Automated identification of diverse sentiment types can be beneficial for many NLP systems such as review summarization and public media analysis. In some of these systems there i...
We propose a novel algorithm for sentiment summarization that takes account of informativeness and readability, simultaneously. Our algorithm generates a summary by selecting and ...
Eye tracking experiments have shown that titles of Web search results play a crucial role in guiding a user’s search process. We present a machine-learned algorithm that trains ...
Tapas Kanungo, Nadia Ghamrawi, Ki Yuen Kim, Lawren...
Evaluating text fragments for positive and negative subjective expressions and their strength can be important in applications such as single- or multi- document summarization, do...