Towards Generating Text Summaries for Entity Chains

  • Shruti Chhabra
  • Srikanta Bedathur
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8416)


Given a large knowledge graph, discovering meaningful relationships between a given pair of entities has gained a lot of attention in the recent times. Most existing algorithms focus their attention on identifying one or more structures –such as relationship chains or subgraphs– between the entities. The burden of interpreting these results, after combining with contextual information and description of relationships, lies with the user. In this paper, we present a framework that eases this burden by generating a textual summary which incorporates the context and description of individual (dyadic) relationships, and combines them to generate a ranked list of summaries. We develop a model that captures key properties of a well-written text, such as coherence and information content. We focus our attention on a special class of relationship structures, two-length entity chains, and show that the generated ranked list of summaries have 79% precision at rank-1. Our results demonstrate that the generated summaries are quite useful to users.


entity chain text summarization relationship queries entity-relationship graphs 


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  1. 1.
    Agrawal, R., Chakraborty, S., Gollapudi, S., Kannan, A., Kenthapadi, K.: Empowering authors to diagnose comprehension burden in textbooks. In: KDD, pp. 967–975 (2012)Google Scholar
  2. 2.
    Anyanwu, K., Sheth, A.: The ρ operator: discovering and ranking associations on the semantic web. ACM SIGMOD Record 31(4), 42–47 (2002)CrossRefGoogle Scholar
  3. 3.
    Bizer, C., Lehmann, J., Kobilarov, G., Auer, S., Becker, C., Cyganiak, R., Hellmann, S.: Dbpedia - a crystallization point for the web of data. Web Semant. 7(3), 154–165 (2009)CrossRefGoogle Scholar
  4. 4.
    Blanco, R., Zaragoza, H.: Finding support sentences for entities. In: SIGIR, pp. 339–346 (2010)Google Scholar
  5. 5.
    Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: A collaboratively created graph database for structuring human knowledge. In: SIGMOD, pp. 1247–1250 (2008)Google Scholar
  6. 6.
    Carlson, A., Betteridge, J., Kisiel, B., Settles, B., Hruschka Jr., E.R., Mitchell, T.M.: Toward an architecture for never-ending language learning. In: AAAI Conf. on Artifical Intelligence (2010)Google Scholar
  7. 7.
    Chhabra, S., Bedathur, S.: Generating text summaries of graph snippets. In: COMAD, pp. 121–124 (2013)Google Scholar
  8. 8.
    Cohen, T., Whitfield, G., Schvaneveldt, R., Mukund, K., Rindflesch, T.: Epiphanet: An interactive tool to support biomedical discoveries. Journal of Biomedical Discovery and Collaboration 5, 21–49 (2010)Google Scholar
  9. 9.
    Etzioni, O., Fader, A., Christensen, J., Soderland, S., Mausam, M.: Open information extraction: The second generation. In: IJCAI, pp. 3–10 (2011)Google Scholar
  10. 10.
    Fang, L., Sarma, A.D., Yu, C., Bohannon, P.: Rex: explaining relationships between entity pairs. Proc. VLDB Endow. 5(3) (2011)Google Scholar
  11. 11.
    Finkel, J.R., Grenager, T., Manning, C.: Incorporating non-local information into information extraction systems by gibbs sampling. In: ACL, pp. 363–370 (2005)Google Scholar
  12. 12.
    Foltz, P.W.: Latent semantic analysis for text-based research. Behavior Research Methods, Instruments, & Computers 28(2), 197–202 (1996)CrossRefGoogle Scholar
  13. 13.
    Foltz, P.W., Kintsch, W., Landauer, T.K.: The measurement of textual coherence with latent semantic analysis. Discourse Processes 25(2-3), 285–307 (1998)CrossRefGoogle Scholar
  14. 14.
    Gray, W.S., Leary, B.E.: What makes a book readable. Univ. Chicago Press (1935)Google Scholar
  15. 15.
    Halaschek, C., Aleman-Meza, B., Arpinar, I.B., Sheth, A.P.: Discovering and ranking semantic associations over a large rdf metabase. In: VLDB, pp. 1317–1320 (2004)Google Scholar
  16. 16.
    Hoffart, J., Suchanek, F.M., Berberich, K., Lewis-Kelham, E., de Melo, G., Weikum, G.: Yago2: Exploring and querying world knowledge in time, space, context, and many languages. In: WWW, pp. 229–232 (2011)Google Scholar
  17. 17.
    Hristovski, D., Friedman, C., Rindflesch, T.C., Peterlin, B.: Exploiting semantic relations for literature-based discovery. In: AMIA Annual Symp., vol. 2006, pp. 349–353 (2006)Google Scholar
  18. 18.
    Hristovski, D., Kastrin, A., Peterlin, B., Rindflesch, T.C.: Combining semantic relations and dna microarray data for novel hypotheses generation. In: Proceedings of the 2009 Workshop of the BioLink Special Interest Group, International Conference on Linking Literature, Information, and Knowledge for Biology, pp. 53–61 (2010)Google Scholar
  19. 19.
    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: ICDM, pp. 193–202 (2007)Google Scholar
  20. 20.
    Kasneci, G., Ramanath, M., Sozio, M., Suchanek, F.M., Weikum, G.: Star: Steiner-tree approximation in relationship graphs. In: ICDE, pp. 868–879 (2009)Google Scholar
  21. 21.
    Kintsch, W., Van Dijk, T.A.: Toward a model of text comprehension and production. Psychological Review 85(5), 363–394 (1978)CrossRefGoogle Scholar
  22. 22.
    Laham, T.K., Laham, D., Foltz, P.W.: Learning human-like knowledge by singular value decomposition: A progress report. In: NIPS, vol. 10, pp. 45–51 (1998)Google Scholar
  23. 23.
    Lapata, M., Barzilay, R.: Automatic evaluation of text coherence: Models and representations. In: IJCAI, pp. 1085–1090 (2005)Google Scholar
  24. 24.
    Lin, C.-Y.: Rouge: A package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL 2004 Workshop, pp. 74–81 (2004)Google Scholar
  25. 25.
    Nakashole, N., Weikum, G., Suchanek, F.: Patty: a taxonomy of relational patterns with semantic types. In: EMNLP, pp. 1135–1145 (2012)Google Scholar
  26. 26.
    Pitler, E., Nenkova, A.: Revisiting readability: a unified framework for predicting text quality. In: EMNLP, pp. 186–195 (2008)Google Scholar
  27. 27.
    Řehůřek, R., Sojka, P.: Software framework for topic modelling with large corpora. In: LREC Workshop on New Challenges for NLP Frameworks, pp. 45–50 (2010)Google Scholar
  28. 28.
    Smalheiser, N.R., Swanson, D.R.: Indomethacin and alzheimer’s disease. Neurology 46(2), 583–583 (1996)CrossRefGoogle Scholar
  29. 29.
    Smalheiser, N.R., Swanson, D.R.: Linking estrogen to alzheimer’s disease an informatics approach. Neurology 47(3), 809–810 (1996)CrossRefGoogle Scholar
  30. 30.
    Srihari, R.K., Xu, L., Saxena, T.: Use of ranked cross document evidence trails for hypothesis generation. In: KDD, pp. 677–686 (2007)Google Scholar
  31. 31.
    Srinivasan, P.: Text mining: generating hypotheses from medline. Journal of American Society for Information Science and Technology 55(5), 396–413 (2004)CrossRefGoogle Scholar
  32. 32.
    Swanson, D.R.: Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science 38(4), 228–233 (1987)CrossRefGoogle Scholar
  33. 33.
    Swanson, D.R., Smalheiser, N.R.: An interactive system for finding complementary literatures: a stimulus to scientific discovery. Artificial Intelligence 91(2), 183–203 (1997)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Shruti Chhabra
    • 1
  • Srikanta Bedathur
    • 1
  1. 1.Indraprastha Institute of Information TechnologyNew DelhiIndia

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