title: Clustering cliques for graph-based summarization of the biomedical research literature.
abstract: BACKGROUND
  Graph-based notions are increasingly used in biomedical data mining and knowledge discovery tasks. In this paper, we present a clique-clustering method to automatically summarize graphs of semantic predications produced from PubMed citations (titles and abstracts).
  
  
  RESULTS
  SemRep is used to extract semantic predications from the citations returned by a PubMed search. Cliques were identified from frequently occurring predications with highly connected arguments filtered by degree centrality. Themes contained in the summary were identified with a hierarchical clustering algorithm based on common arguments shared among cliques. The validity of the clusters in the summaries produced was compared to the Silhouette-generated baseline for cohesion, separation and overall validity. The theme labels were also compared to a reference standard produced with major MeSH headings.
  
  
  CONCLUSIONS
  For 11 topics in the testing data set, the overall validity of clusters from the system summary was 10% better than the baseline (43% versus 33%). While compared to the reference standard from MeSH headings, the results for recall, precision and F-score were 0.64, 0.65, and 0.65 respectively.
authors.affiliation: Department of Medical Informatics, China Medical University, Shenyang, Liaoning 110001, China. zhanghan@mail.cmu.edu.cn
authors.forename: Han
authors.initials: H
authors.lastname: Zhang
authors.forename: Marcelo
authors.initials: M
authors.lastname: Fiszman
authors.forename: Dongwook
authors.initials: D
authors.lastname: Shin
authors.forename: Bartlomiej
authors.initials: B
authors.lastname: Wilkowski
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rindflesch
mesh_terms: D000465:Algorithms; D035843:Biomedical Research; D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D046650:Medical Subject Headings; D039781:PubMed; D012660:Semantics