Recent trends in hierarchic document clustering: a critical review
Information Processing and Management: an International Journal
Web document clustering: a feasibility demonstration
Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval
Information Retrieval
Genes, Themes, and Microarrays: Using Information Retrieval for Large-Scale Gene Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
The automatic creation of literature abstracts
IBM Journal of Research and Development
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In this paper, we introduce a coherent biomedical literature clustering and summarization approach that employs a graphical representation method for text using a biomedical ontology. The key of the approach is to construct document cluster models as semantic chunks capturing the core semantic relationships in the ontology-enriched scale-free graphical representation of documents. These document cluster models are used for both document clustering and text summarization by constructing Text Semantic Interaction Network (TSIN). Our extensive experimental results indicate our 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. The primary contribution of this paper is we introduce a coherent biomedical literature clustering and summarization approach that takes advantage of ontology-enriched graphical representations. Our approach significantly improves the quality of document clusters and understandability of documents through summaries.