An analysis of automatic content selection algorithms for spoken dialogue system summaries

Previous work on information presentation in dialogue systems has argued that a user model is essential for selecting utterance content. Other work claims that the ability to browse the data is critical for supporting information-seeking behaviors in information retrieval applications, but does not specify how to provide browsing support. In this work, we test the hypothesis that automatically constructed summaries based on the current dialogue state and provided incrementally during the dialogue can support users\´ browsing activities. We examine the impact of three factors on summary efficacy: (1) the number and type of attributes selected for summary construction; (2) the use of a decision theoretic user model; and (3) the use of association rules derived automatically from the database subset currently in-focus. Our experimental results show that equally effective summaries can be constructed using either a combination of a user model with association rules, or the "Refiner" method described in previous work.