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Group decision making occurs when individuals collectively choose from a set of alternatives based on individual preferences. In these ubiquitous situations, it can be helpful for decision makers to visually model and compare stakeholder preferences in order to better understand others' points of view and reach consensus. Although a number of collaboration support tools allow preference inspection in some form, they are rarely based on a comprehensive understanding of the needs of group decision makers. The goal of our work is to study these demands, develop abstractions to model them, and create a framework to inform the design and assessment of existing and future tools. First, guided by decision analysis theory, we examine a diverse set of group decision making scenarios, characterizing variations in problem formulation, analysis goals, and situational features. Second, we amalgamate these findings into data and task abstractions that can be used to relate specific scenarios to the language of visualization. Finally, we use this framework to assess existing preference visualization tools in order to shed light on areas for future work in supporting group decision making.

We define and motivate the problem of summarizing partial email threads. This problem introduces the challenge of generating reference summaries for partial threads when human annotation is only available for the threads as a whole, particularly when the human-selected sentences are not uniformly distributed within the threads. We propose an oracular algorithm for generating these reference summaries with arbitrary length, and we are making the resulting dataset publicly available 1 . In addition, we apply a recent unsupervised method based on Bayesian Surprise that incorporates background knowledge into partial thread summarization, extend it with conversational features, and modify the mechanism by which it handles redundancy. Experiments with our method indicate improved performance over the baseline for shorter partial threads; and our results suggest that the potential benefits of background knowledge to partial thread summarization should be further investigated with larger datasets.

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