A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method.
BACKGROUND
A huge amount of biomedical textual information has been produced and collected in MEDLINE for decades. In order to easily utilize biomedical information in the free text, document clustering and text summarization together are used as a solution for text information overload problem. In this paper, we introduce a coherent graph-based semantic clustering and summarization approach for biomedical literature.


RESULTS
Our extensive experimental results show the approach shows 45% cluster quality improvement and 72% clustering reliability improvement, in terms of misclassification index, over Bisecting K-means as a leading document clustering approach. In addition, our approach provides concise but rich text summary in key concepts and sentences.


CONCLUSIONS
Our coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations significantly improves the quality of document clusters and understandability of documents through summaries.
Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, USA. yooil@health.missouri.edu
Illhoi
I
Yoo
Xiaohua
X
Hu
Il-Yeol
IY
Song
D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003628:Database Management Systems; D016247:Information Storage and Retrieval / Q000379:methods; D016239:MEDLINE; D009323:Natural Language Processing; D010363:Pattern Recognition, Automated / Q000379:methods; D010506:Periodicals as Topic; D012660:Semantics; D014584:User-Computer Interface