Evaluating Different Similarity Measures for Automatic Biomedical Text Summarization

  • Mozhgan Nasr Azadani
  • Nasser Ghadiri
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)


Automatic biomedical text summarization is maturing and can provide a solution for biomedical researchers to access the information they need efficiently. Biomedical summarization approaches often rely on the similarity measure to model the source document, mainly when they employ redundancy removal or graph structures. In this paper, we examine the impact of the similarity measure on the performance of the summarization methods. We model the document as a weighted graph. Various similarity measures are used to build different graphs based on biomedical concepts, semantic types and a combination of them. We next use the graphs to generate and evaluate the automatic summaries. The results suggest that the selection of the similarity measure has a substantial effect on the quality of the summaries (≈37% improvement in ROUGE-2 metric, and ≈29% in ROUGE-SU4). The results also demonstrate that exploiting both biomedical concepts and semantic types yields slightly better performance.


Biomedical similarity measure Graph-based text summarization Sentence similarity 


  1. 1.
    MEDLINE. Accessed 7 Oct 2017
  2. 2.
    Afantenos, S., Karkaletsis, V., Stamatopoulos, P.: Summarization from medical documents: a survey. J. Artif. Intel. Med. 33, 157–177 (2005)CrossRefGoogle Scholar
  3. 3.
    Fleuren, W.W.M., Alkema, W.: Application of text mining in the biomedical domain. J. Meth. 74, 97–106 (2015)CrossRefGoogle Scholar
  4. 4.
    Jones, K.S.: Automatic summarising: the state of the art. J. Inf. Process. Manage. 43, 1449–1481 (2007)CrossRefGoogle Scholar
  5. 5.
    Mishra, R., Bian, J., Fiszman, M., Weir, C.R., Jonnalagadda, S., Mostafa, J., et al.: Text summarization in the biomedical domain: a systematic review of recent research. J. Biomed. Inform. 52, 457–467 (2014)CrossRefGoogle Scholar
  6. 6.
    Reeve, L.H., Han, H., Nagori, S., Yang, J.C., Schwimmer, T.A.: Concept frequency distribution in biomedical text summarization. In: Proceedings of the 15th ACM International Conference on Information and Knowledge Management, pp. 604–611 (2006) Google Scholar
  7. 7.
    Sarkar, K.: Using domain knowledge for text summarization in medical domain. Int. J. Recent Trends Eng. 1, 200–205 (2009)Google Scholar
  8. 8.
    Plaza, L., Díaz, A., Gervás, P.: A semantic graph-based approach to biomedical summarisation. J. Artif. Intell. Med. 53, 1–14 (2011)CrossRefGoogle Scholar
  9. 9.
    Lin, C.-Y.: Rouge: a package for automatic evaluation of summaries. In: Proceedings of Workshop on Text Summarization Branches Out, Workshop of ACL (2004)Google Scholar
  10. 10.
    Gambhir, M., Gupta, V.: Recent automatic text summarization techniques: a survey. J. Artif. Intell. Rev. 47, 1–66 (2017)CrossRefGoogle Scholar
  11. 11.
    Yao, J.-G., Wan, X., Xiao, J.: Recent advances in document summarization. J. Knowl. Inf. Syst. 53, 297–336 (2017)CrossRefGoogle Scholar
  12. 12.
    Nelson, S.J., Powell, T., Humphreys, B.L.: The Unified Medical Language System (UMLS) project, Encyclopedia of library (2002)Google Scholar
  13. 13.
    Reeve, L.H., Han, H., Brooks, A.D.: BioChain: lexical chaining methods for biomedical text summarization. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 180–184. ACM (2006)Google Scholar
  14. 14.
    Yoo, I., Hu, X., Song, I.-Y.: A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method. J. BMC Bioinform. 8, S4 (2007)CrossRefGoogle Scholar
  15. 15.
    Menendez, H.D., Plaza, L., Camacho, D.: A genetic graph-based clustering approach to biomedical summarization. In: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics, pp. 1–8. ACM (2013)Google Scholar
  16. 16.
    Fiszman, M., Demner-Fushman, D., Kilicoglu, H., Rindflesch, T.C.: Automatic summarization of MEDLINE citations for evidence-based medical treatment: a topic-oriented evaluation. J. Biomed. Inform. 42, 801–813 (2009)CrossRefGoogle Scholar
  17. 17.
    Zhang, H., Fiszman, M., Shin, D., Wilkowski, B., Rindflesch, T.C.: Clustering cliques for graph-based summarization of the biomedical research literature. J. BMC Bioinform. 14, 182 (2013)CrossRefGoogle Scholar
  18. 18.
    Zhang, H., Fiszman, M., Shin, D., Miller, C.M., Rosemblat, G., Rindflesch, T.C.: Degree centrality for semantic abstraction summarization of therapeutic studies. J. Biomed. Inform. 44, 830–838 (2011)CrossRefGoogle Scholar
  19. 19.
    Reeve, L.H., Han, H., Brooks, A.D.: The use of domain-specific concepts in biomedical text summarization. J. Inf. Process. Manage. 43, 1765–1776 (2007)CrossRefGoogle Scholar
  20. 20.
    Plaza, L.: Comparing different knowledge sources for the automatic summarization of biomedical literature. J. Biomed. Inform. 52, 319–328 (2014)CrossRefGoogle Scholar
  21. 21.
    Plaza, L., Carrillo-de-Albornoz, J.: Evaluating the use of different positional strategies for sentence selection in biomedical literature summarization. J. BMC Bioinform. 14, 71 (2013)CrossRefGoogle Scholar
  22. 22.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques, 3rd edn. Morgan Kaufmann Publishers, Burlington (2011)zbMATHGoogle Scholar
  23. 23.
    Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceeding of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404–411 (2004)Google Scholar
  24. 24.
    National Library of Medicine. MetaMap portal. Accessed 25 May 2017
  25. 25.
    Doherty, J.L., Owen, M.J.: Genomic insights into the overlap between psychiatric disorders: implications for research and clinical practice. J. Genome Med. 6, 29 (2014)CrossRefGoogle Scholar
  26. 26.
    BioMed Central. Accessed 15 Mar 2017
  27. 27.
    Mitkov, R.: The Oxford Handbook of Computational Linguistics. Oxford University Press, Oxford (2003)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringIsfahan University of TechnologyIsfahanIran

Personalised recommendations