Advertisement

iDVS: An Interactive Multi-document Visual Summarization System

  • Yi Zhang
  • Dingding Wang
  • Tao Li
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6913)

Abstract

Multi-document summarization is a fundamental tool for understanding documents. Given a collection of documents, most of existing multi- document summarization methods automatically generate a static summary for all the users using unsupervised learning techniques such as sentence ranking and clustering. However, these methods almost exclude human from the summarization process. They do not allow for user interaction and do not consider users’ feedback which delivers valuable information and can be used as the guidance for summarization. Another limitation is that the generated summaries are displayed in textual format without visual representation. To address the above limitations, in this paper, we develop iDVS, a visualization-enabled multi-document summarization system with users’ interaction, to improve the summarization performance using users’ feedback and to assist users in document understanding using visualization techniques. In particular, iDVS uses a new semi-supervised document summarization method to dynamically select sentences based on users’ interaction. To this regard, iDVS tightly integrates semi-supervised learning with interactive visualization for document summarization. Comprehensive experiments on multi-document summarization using benchmark datasets demonstrate the effectiveness of iDVS, and a user study is conducted to evaluate the users’ satisfaction.

Keywords

interactive multi-document summarization visualization 

References

  1. 1.
    Agarwal, G., Kempe, D.: Modularity-maximizing graph communities via mathematical programming. The European Physical Journal B - Condensed Matter and Complex Systems 66(3), 409–418 (2008)CrossRefzbMATHMathSciNetGoogle Scholar
  2. 2.
    Allan, J., Leouski, A.V., Swan, R.C.: Interactive cluster visualization for information retrieval. In: ECDL (1998)Google Scholar
  3. 3.
    Ando, R., Boguraev, B., Byrd, R., Neff, M.: Visualization-enabled multi-document summarization by iterative residual rescaling. Nat. Lang. Eng. 11(1), 67–86 (2005)CrossRefGoogle Scholar
  4. 4.
    Belkin, M., Niyogi, P.: Towards a theoretical foundation for laplacian-based manifold methods. In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS (LNAI), vol. 3559, pp. 486–500. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  5. 5.
    Chapelle, O., Schölkopf, B., Zien, A. (eds.): Semi-Supervised Learning. MIT Press, Cambridge (2006)Google Scholar
  6. 6.
    Chen, K., Liu, L.: Vista: validating and refining clusters via visualization. Information Visualization 3(4), 257–270 (2004)CrossRefGoogle Scholar
  7. 7.
    Chen, K., Liu, L.: ivibrate: Interactive visualization-based framework for clustering large datasets. ACM Trans. Inf. Syst. 24(2), 245–294 (2006)CrossRefGoogle Scholar
  8. 8.
    Conroy, J., O’Leary, D.: Text summarization via hidden markov models. In: SIGIR, pp. 406–407 (2001)Google Scholar
  9. 9.
    Ding, C., Jin, R., Li, T., Simon, H.D.: A learning framework using green’s function and kernel regularization with application to recommender system. In: SIGKDD (2007)Google Scholar
  10. 10.
    Don, A., Zheleva, E., Gregory, M., Tarkan, S., Auvil, L., Clement, T., Shneiderman, B., Plaisant, C.: Discovering interesting usage patterns in text collections: integrating text mining with visualization. In: CIKM, pp. 213–222 (2007)Google Scholar
  11. 11.
    Erkan, G., Radev, D.: Lexpagerank: Prestige in multi-document text summarization. In: EMNLP (2004)Google Scholar
  12. 12.
    Goldstein, J., Kantrowitz, M., Mittal, V., Carbonell, J.: Summarizing text documents: Sentence selection and evaluation metrics. In: SIGIR, pp. 121–128 (1999)Google Scholar
  13. 13.
    Gong, Y., Liu, X.: Generic text summarization using relevance measure and latent semantic analysis. In: SIGIR, pp. 75–95 (2001)Google Scholar
  14. 14.
    Grinstain, G., Ankerst, M., Keim, D.: Visual data mining: Background, applications, ad drug discovery applications. In: SIGMOD (1999)Google Scholar
  15. 15.
    Havre, S., Hetzler, E., Whitney, P., Nowell, L.: Themeriver: Visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics 8(1), 9–20 (2002)CrossRefGoogle Scholar
  16. 16.
    Hearst, M.A.: Tilebars: visualization of term distribution information in full text information access. In: CHI, pp. 59–66 (1995)Google Scholar
  17. 17.
    Hein, M., Audibert, J., Von Luxburg, U.: From graphs to manifolds - weak and strong pointwise consistency of graph laplacians. In: Auer, P., Meir, R. (eds.) COLT 2005. LNCS (LNAI), vol. 3559, pp. 470–485. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  18. 18.
    Hinneburg, A., Keim, D., Wawryniuk, M.: Visual mining of high-dimensional data. IEEE Computer Graphics and Applications (1999)Google Scholar
  19. 19.
    Hu, M., Sun, A., Lim, E.-P.: Comments-oriented document summarization: understanding documents with readers’ feedback. In: SIGIR, pp. 291–298 (2008)Google Scholar
  20. 20.
    Jiao, B., Yang, L., Xu, J., Wu, F.: Visual summarization of web pages. In: SIGIR, pp. 499–506 (2010)Google Scholar
  21. 21.
    Kerr, B.: Thread arcs: an email thread visualization. In: InfoVis, pp. 211–218 (2003)Google Scholar
  22. 22.
    Lee, D.D., Seung, H.S.: Algorithms for non-negative matrix factorization. In: NIPS (2001)Google Scholar
  23. 23.
    Lin, C.-Y., Hovy, E.: From single to multi-document summarization: A prototype system and its evaluation. In: ACL, pp. 457–464 (2001)Google Scholar
  24. 24.
    Lin, C.-Y., Hovy, E.: Automatic evaluation of summaries using n-gram co-occurrence statistics. In: NLT-NAACL, pp. 71–78 (2003)Google Scholar
  25. 25.
    Liu, S., Zhou, M.X., Pan, S., Qian, W., Cai, W., Lian, X.: Interactive, topic-based visual text summarization and analysis. In: CIKM, pp. 543–552 (2009)Google Scholar
  26. 26.
    Nardi, B.A., Whittaker, S., Isaacs, E., Creech, M., Johnson, J., Hainsworth, J.: Integrating communication and information through contactmap. Commun. ACM 45(4), 89–95 (2002)CrossRefGoogle Scholar
  27. 27.
    Noack, A.: Modularity clustering is force-direced layout. Physical Review E 79, 026102 (2009)CrossRefGoogle Scholar
  28. 28.
    Perer, A., Smith, M.A.: Contrasting portraits of email practices: visual approaches to reflection and analysis. In: AVI 2006, pp. 389–395 (2006)Google Scholar
  29. 29.
    Radev, D., Jing, H., Stys, M., Tam, D.: Centroid-based summarization of multiple documents. In: Information Processing and Management, pp. 919–938 (2004)Google Scholar
  30. 30.
    Rennison, E.: Galaxy of news: an approach to visualizing and understanding expansive news landscapes. In: UIST 1994, pp. 3–12 (1994)Google Scholar
  31. 31.
    Shen, D., Sun, J.-T., Li, H., Yang, Q., Chen, Z.: Document summarization using conditional random fields. In: IJCAI, pp. 2862–2867 (2007)Google Scholar
  32. 32.
    Stasko, J., Görg, C., Liu, Z.: Jigsaw: supporting investigative analysis through interactive visualization. Information Visualization 7(2), 118–132 (2008)CrossRefGoogle Scholar
  33. 33.
    Wang, D., Li, T., Zhu, S., Ding, C.H.Q.: Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization. In: SIGIR, pp. 307–314 (2008)Google Scholar
  34. 34.
    Wattenberg, M., Viégas, F.B.: The word tree, an interactive visual concordance. IEEE Transactions on Visualization and Computer Graphics 14(6), 1221–1228 (2008)CrossRefGoogle Scholar
  35. 35.
    Wong, K.-F., Wu, M., Li, W.: Extractive summarization using supervised and semi-supervised learning. In: Coling (2008)Google Scholar
  36. 36.
    Yang, L.: n23tool: A tool for exploring large relational datasets through 3d dynamic projections. In: CIKM (2000)Google Scholar
  37. 37.
    Yih, W.-T., Goodman, J., Vanderwende, L., Suzuki, H.: Multi-document summarization by maximizing informative content-words. In: IJCAI, pp. 1776–1782 (2007)Google Scholar
  38. 38.
    Zhou, D., Bousquet, O., Navin Lal, T., Weston, J., Schölkopf, B.: Learning with local and global consistency. In: NIPS, vol. 16, pp. 321–328 (2004)Google Scholar
  39. 39.
    Zhu, X.: Semi-supervised learning literature survey. Technical report, Computer Sciences, University of Wisconsin-Madison (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yi Zhang
    • 1
  • Dingding Wang
    • 1
  • Tao Li
    • 1
  1. 1.School of Computer ScienceFlorida International UniversityMiamiUSA

Personalised recommendations