Degree Centrality for Semantic Abstraction Summarization of Therapeutic Studies

Overview

Journal J Biomed Inform

Publisher Elsevier

Specialty Medical Informatics

Date 2011 May 18

PMID 21575741

Citations 13

Authors

Han Zhang

Marcelo Fiszman

Dongwook Shin

Christopher M Miller

Graciela Rosemblat

Thomas C Rindflesch

Affiliations

Soon will be listed here.

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).

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