Utilizing Review Summarization in a Spoken Recommendation System
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 summ...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
| Published: |
Association for Computational Linguistics
2011
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| Online Access: | http://hdl.handle.net/1721.1/62198 https://orcid.org/0000-0003-2602-0862 https://orcid.org/0000-0001-8191-1049 |
| Summary: | 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. |
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