Interactive, topic-based visual text summarization and analysis

  • Authors:

  • Shixia Liu;Michelle X. Zhou;Shimei Pan;Weihong Qian;Weijia Cai;Xiaoxiao Lian

  • Affiliations:

  • IBM Research Lab, Beijing, China;IBM Research Lab, Beijing, China;IBM T. J. Watson Research Center, Hawthorne, NY, USA;IBM China Research Lab, Beijing, China;IBM China Research Lab, Beijing, China;IBM China Research Lab, Beijing, China

  • Venue:
  • Proceedings of the 18th ACM conference on Information and knowledge management
  • Year:
  • 2009

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Abstract

We are building an interactive, visual text analysis tool that aids users in analyzing a large collection of text. Unlike existing work in text analysis, which focuses either on developing sophisticated text analytic techniques or inventing novel visualization metaphors, ours is tightly integrating state-of-the-art text analytics with interactive visualization to maximize the value of both. In this paper, we focus on describing our work from two aspects. First, we present the design and development of a time-based, visual text summary that effectively conveys complex text summarization results produced by the Latent Dirichlet Allocation (LDA) model. Second, we describe a set of rich interaction tools that allow users to work with a created visual text summary to further interpret the summarization results in context and examine the text collection from multiple perspectives. As a result, our work offers two unique contributions. First, we provide an effective visual metaphor that transforms complex and even imperfect text summarization results into a comprehensible visual summary of texts. Second, we offer users a set of flexible visual interaction tools as the alternatives to compensate for the deficiencies of current text summarization techniques. We have applied our work to a number of text corpora and our evaluation shows the promise of the work, especially in support of complex text analyses.