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Utilizing Review Summarization in a Spoken Recommendation System

Jingjing Liu, Stephanie Seneff, and Victor Zue

Abstract

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.

Details

Publication typeProceedings
PublisherSIGDIAL 2010
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