Rationale-based Opinion Summarization
Haoyuan Li Snigdha Chaturvedi
Opinion summarization aims to generate concise summaries that present popular
opinions of a large group of reviews. However, these summaries can be too
generic and lack supporting details. To address these issues, we propose a new
paradigm for summarizing reviews, rationale-based opinion summarization.
Rationale-based opinion summaries output the representative opinions as well as
one or more corresponding rationales. To extract good rationales, we define
four desirable properties: relatedness, specificity, popularity, and diversity
and present a Gibbs-sampling-based method to extract rationales. Overall, we
propose RATION, an unsupervised extractive system that has two components: an
Opinion Extractor (to extract representative opinions) and Rationales Extractor
(to extract corresponding rationales). We conduct automatic and human
evaluations to show that rationales extracted by RATION have the proposed
properties and its summaries are more useful than conventional summaries. The
implementation of our work is available at
https://github.com/leehaoyuan/RATION.