Utilizing Review Summarization in a Spoken Recommendation System
Jun Liu, Stephanie Seneff, Victor W. Zue · DSpace@MIT (Massachusetts Institute of Technology) · 2010
In this paper we present a framework for spoken recommendation systems. To provide reliable recommendations to users, we incorporate a review summarization technique which extracts informative opinion summaries from grass-roots users‘ reviews. The dialogue system then utilizes these review summaries to support both quality-based opinion inquiry and feature- specific entity search. We propose a probabilistic language generation approach to automatically creating recommendations in spoken natural language from the text-based opinion summaries. A user study in the restaurant domain shows that the proposed approaches can effectively generate reliable and helpful recommendations in human-computer conversations.