Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
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We introduce a method that integrates biomedical literature clustering and summarization using biomedical ontology. The core of the approach is to identify 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 on document assignment and text summarization on the construction of Text Semantic Interaction Network (TSIN). Our experimental results show our approach is superior to traditional approaches including Bisecting K-means as a leading document clustering approach in terms of cluster quality and clustering reliability. In addition, our approach provides concise but rich text summary in key concepts and sentences.