Degree centrality for semantic abstraction summarization of therapeutic studies

  • Authors:

  • Han Zhang;Marcelo Fiszman;Dongwook Shin;Christopher M. Miller;Graciela Rosemblat;Thomas C. Rindflesch

  • Affiliations:

  • Department of Medical Informatics, China Medical University, Shenyang, China and National Library of Medicine, National Institutes of Health, Bethesda, MD, United States;National Library of Medicine, National Institutes of Health, Bethesda, MD, United States;National Library of Medicine, National Institutes of Health, Bethesda, MD, United States;National Library of Medicine, National Institutes of Health, Bethesda, MD, United States;National Library of Medicine, National Institutes of Health, Bethesda, MD, United States;National Library of Medicine, National Institutes of Health, Bethesda, MD, United States

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2011

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Abstract

Automatic summarization has been proposed to help manage the results of biomedical information retrieval systems. Semantic MEDLINE, for example, summarizes semantic predications representing assertions in MEDLINE citations. Results are presented as a graph which maintains links to the original citations. Graphs summarizing more than 500 citations are hard to read and navigate, however. We exploit graph theory for focusing these large graphs. The method is based on degree centrality, which measures connectedness in a graph. Four categories of clinical concepts related to treatment of disease were identified and presented as a summary of input text. A baseline was created using term frequency of occurrence. The system was evaluated on summaries for treatment of five diseases compared to a reference standard produced manually by two physicians. The results showed that recall for system results was 72%, precision was 73%, and F-score was 0.72. The system F-score was considerably higher than that for the baseline (0.47).