Utilizing review summarization in a spoken recommendation system

  • Authors:
  • Jingjing Liu;Stephanie Seneff;Victor Zue

  • Affiliations:
  • MIT Computer Science & Artificial Intelligence Laboratory, Cambridge;MIT Computer Science & Artificial Intelligence Laboratory, Cambridge;MIT Computer Science & Artificial Intelligence Laboratory, Cambridge

  • Venue:
  • SIGDIAL '10 Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue
  • Year:
  • 2010

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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.