Visually Summarizing Semantic Evolution in Document Streams with Topic Table

  • André Gohr
  • Myra Spiliopoulou
  • Alexander Hinneburg
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 272)


We propose a visualization technique for summarizing contents of document streams, such as news or scientific archives. The content of streaming documents change over time and so do themes the documents are about. Topic evolution is a relatively new research subject that encompasses the unsupervised discovery of thematic subjects in a document collection and the adaptation of these subjects as new documents arrive. While many powerful topic evolution methods exist, the combination of learning and visualization of the evolving topics has been less explored, although it is indispensable for understanding a dynamic document collection.

We propose Topic Table, a visualization technique that builds upon topic modeling for deriving a condensed representation of a document collection. Topic Table captures important and intuitively comprehensible aspects of a topic over time: the importance of the topic within the collection, the words characterizing this topic, the semantic changes of a topic from one timepoint to the next. As an example, we visualize content of the NIPS proceedings from 1987 to 1999.


Visualization Topic modeling Evolving topics Summarizing dynamic document collections Stream analysis 


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  1. 1.
    AlSumait, L., Barbara, D., Domeniconi, C.: On-line LDA: adaptive topic models for mining text streams with applications to topic detection and tracking. In: ICDM (2008)Google Scholar
  2. 2.
    Blei, D., Lafferty, J.: Dynamic topic models. In: ICML (2006)Google Scholar
  3. 3.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  4. 4.
    Boyd-Graber, J., Chang, J., Gerrish, S., Wang, C., Blei, D.: Reading tea leaves: How humans interpret topic models. In: Neural Information Processing Systems, NIPS (2009)Google Scholar
  5. 5.
    Chou, T.-C., Chen, M.C.: Using incremental PLSI for threshold-resilient online event analysis. IEEE Trans. on Knowl. and Data Eng. 20(3), 289–299 (2008)CrossRefGoogle Scholar
  6. 6.
    Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, Series B (Methodological) 39(1), 1–38 (1977)MathSciNetMATHGoogle Scholar
  7. 7.
    Ferlez, J., Faloutsos, C., Leskovec, J., Mladenic, D., Grobelnik, M.: Monitoring network evolution using MDL. In: Proceedings of IEEE Int. Conf. on Data Engineering (ICDE 2008). IEEE (2008)Google Scholar
  8. 8.
    Gohr, A., Hinneburg, A., Schult, R., Spiliopoulou, M.: Topic evolution in a stream of documents. In: SIAM Data Mining Conf. (SDM 2009), Reno, NV, pp. 378–385 (April-May 2009)Google Scholar
  9. 9.
    Gohr, A., Spiliopoulou, M., Hinneburg, A.: Visually summarizing the evolution of documents under a social tag. In: International Conf. on Knowledge Discovery and Information Retrieval (KDIR 2010), Valencia, Spain, pp. 85–94. SciTePress Digital Library (October 2010)Google Scholar
  10. 10.
    Guha, S., Meyerson, A., Mishra, N., Motwani, R., O’Callaghan, L.: Clustering data streams: Theory and practice. IEEE Trans. of Knowlende and Data Eng. 15(3), 515–528 (2003)CrossRefGoogle Scholar
  11. 11.
    Havre, S., Hetzler, E., Whitney, P., Nowell, L.: ThemeRiver: Visualizing thematic changes in large document collections. IEEE Trans. Visualization and Computer Graphics 8(1), 9–20 (2002)CrossRefGoogle Scholar
  12. 12.
    Hofmann, T.: Unsupervised learning by probabilistic latent semantic analysis. Machine Learning 42(1), 177–196 (2001)MATHCrossRefGoogle Scholar
  13. 13.
    Ipeirotis, P., Ntoulas, A., Cho, J., Gravano, L.: Modeling and managing content changes in text databases. In: Proceedings of the IEEE Int. Conf. on Data Engineering, ICDE 2005 (2005)Google Scholar
  14. 14.
    Jin, W., Srihari, R.K., Ho, H.H., Wu, X.: Improving knowledge discovery in document collections through combining text retrieval and link analysis techniques. In: Proceedings of the 2007 Seventh IEEE International Conference on Data Mining, pp. 193–202. IEEE Computer Society, Washington, DC, USA (2007)CrossRefGoogle Scholar
  15. 15.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2009, pp. 497–506. ACM, New York (2009)CrossRefGoogle Scholar
  16. 16.
    Mei, Q., Shen, X., Zhai, C.: Automatic labeling of multinomial topic models. In: KDD, pp. 490–499 (2007)Google Scholar
  17. 17.
    Mei, Q., Zhai, C.: Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In: SIGKDD, pp. 198–207. ACM, New York (2005)Google Scholar
  18. 18.
    Newman, D., Baldwin, T., Cavedon, L., Huang, E., Karimi, S., Martinez, D., Scholer, F., Zobel, J.: Visualizing search results and document collections using topic maps. Web Semantics: Science, Services and Agents on the World Wide Web 8(2-3), 169–175 (2010); Bridging the Gap–Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0; The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life SciencesGoogle Scholar
  19. 19.
    Cleveland, W.S.: The Elements of Graphing Data. Hobart Press, Summit (1985/1994)Google Scholar
  20. 20.
    Wang, C., Blei, D., Heckerman, D.: Continuous Time Dynamic Topic Models. In: Proceedings of ICML (2008)Google Scholar
  21. 21.
    Wang, X., McCallum, A.: Topics over time: a non-markov continuous-time model of topical trends. In: SIGKDD, pp. 424–433. ACM (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • André Gohr
    • 1
  • Myra Spiliopoulou
    • 2
  • Alexander Hinneburg
    • 1
  1. 1.Martin Luther UniversityHalle SaaleGermany
  2. 2.Otto-von-Guericke UniversityMagdeburgGermany

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