This article is part of the supplement: First International Workshop on Text Mining in Bioinformatics (TMBio) 2006
A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method
1 Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, USA
2 College of Information Science and Technology, Drexel University, USA
BMC Bioinformatics 2007, 8(Suppl 9):S4 doi:10.1186/1471-2105-8-S9-S4Published: 27 November 2007
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.
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.
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.