Similarity evaluation of sets of linguistic summaries

Creating linguistic summaries of data has been a goal of the artificial and computational intelligence communities for many years. Summaries of written text have garnered the most attention. More recently, creating summaries of imagery and other sensed data has become important as a means of compressing large amounts of data and communicating with humans. In this paper, we consider the question of comparing sets of summaries generated from sensed data. In an earlier work, we developed a metric between individual protoform-based summaries; and here, as a next step, we propose aggregation methods to fuse these individual distances. We provide a case study from eldercare where the goal is to compare different nighttime patterns for change detection. C © 2012 Wiley Periodicals, Inc.