Assessing Topic Representations for Gist-Forming

Abstract

As topic modeling has grown in popularity, tools for visualizing the process have become increasingly common. Though these tools support a variety of different tasks, they generally have a view or module that conveys the contents of an individual topic. These views support the important task of gist-forming: helping the user build a cohesive overall sense of the topic's semantic content that can be generalized outside the specific subset of words that are shown. There are a number of factors that affect these views, including the visual encoding used, the number of topic words included, and the quality of the topics themselves. To our knowledge, there has been no formal evaluation comparing the ways in which these factors might change users' interpretations. In a series of crowdsourced experiments, we sought to compare features of visual topic representations in their suitability for gist-forming. We found that gist-forming ability is remarkably resistant to changes in visual representation, though it deteriorates with topics of lower quality.