DialogueOriented Review Summary Generation for Spoken Dialogue Recommendation Systems

DialogueOriented Review Summary Generation for Spoken Dialogue Recommendation Systems (2010)

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by Jingjing Liu , Stephanie Seneff , Victor Zue

Venue:	In Proc. NAACL-HLT
Citations:	2 - 2 self

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Datum	Value	Source
TITLE	DialogueOriented Review Summary Generation for Spoken Dialogue Recommendation Systems	INFERENCE
AUTHOR NAME	Jingjing Liu	SVM HeaderParse 0.2
AUTHOR AFFIL	MIT Computer Science & Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	32 Vassar Street, Cambridge, MA 02139	SVM HeaderParse 0.2
AUTHOR NAME	Stephanie Seneff	SVM HeaderParse 0.2
AUTHOR AFFIL	MIT Computer Science & Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	32 Vassar Street, Cambridge, MA 02139	SVM HeaderParse 0.2
AUTHOR NAME	Victor Zue	SVM HeaderParse 0.2
AUTHOR AFFIL	MIT Computer Science & Artificial Intelligence Laboratory	SVM HeaderParse 0.2
AUTHOR ADDR	32 Vassar Street, Cambridge, MA 02139	SVM HeaderParse 0.2
ABSTRACT	In this paper we present an opinion summarization technique in spoken dialogue systems. Opinion mining has been well studied for years, but very few have considered its application in spoken dialogue systems. Review summarization, when applied to real dialogue systems, is much more complicated than pure text-based summarization. We conduct a systematic study on dialogue-system-oriented review analysis and propose a three-level framework for a recommendation dialogue system. In previous work we have explored a linguistic parsing approach to phrase extraction from reviews. In this paper we will describe an approach using statistical models such as decision trees and SVMs to select the most representative phrases from the extracted phrase set. We will also explain how to generate informative yet concise review summaries for dialogue purposes. Experimental results in the restaurant domain show that the proposed approach using decision tree algorithms achieves an outperformance of 13% compared to SVM models and an improvement of 36 % over a heuristic rule baseline. Experiments also show that the decision-treebased phrase selection model can achieve rather reliable predictions on the phrase label, comparable to human judgment. The proposed statistical approach is based on domain-independent learning features and can be extended to other domains effectively. 1	SVM HeaderParse 0.2
YEAR	2010	INFERENCE
VENUE	In Proc. NAACL-HLT	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
CITATIONS	24 found	ParsCit 1.0