The user model-based summarize and refine approach improves information presentation in spoken dialog systems

A common task for spoken dialog systems (SDS) is to help users select a suitable option (e.g., flight, hotel, and restaurant) from the set of options available. As the number of options increases, the system must have strategies for generating summaries that enable the user to browse the option space efficiently and successfully. In the user-model based summarize and refine approach (UMSR, Demberg and Moore, 2006), options are clustered to maximize utility with respect to a user model, and linguistic devices such as discourse cues and adverbials are used to highlight the trade-offs among the presented items. In a Wizard-of-Oz experiment, we show that the UMSR approach leads to improvements in task success, efficiency, and user satisfaction compared to an approach that clusters the available options to maximize coverage of the domain (Polifroni et al., 2003). In both a laboratory experiment and a web-based experimental paradigm employing the Amazon Mechanical Turk platform, we show that the discourse cues in UMSR summaries help users compare different options and choose between options, even though they do not improve verbatim recall. This effect was observed for both written and spoken stimuli.