Paper: Optimizing Informativeness and Readability for Sentiment Summarization

ACL ID P10-2060
Title Optimizing Informativeness and Readability for Sentiment Summarization
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2010
Authors

We propose a novel algorithm for senti- ment summarization that takes account of informativeness and readability, simulta- neously. Our algorithm generates a sum- mary by selecting and ordering sentences taken from multiple review texts according to two scores that represent the informa- tiveness and readability of the sentence or- der. The informativeness score is defined by the number of sentiment expressions and the readability score is learned from the target corpus. We evaluate our method by summarizing reviews on restaurants. Our method outperforms an existing al- gorithm as indicated by its ROUGE score and human readability experiments.