Journal article
A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method
BMC bioinformatics, v 8 Suppl 9(9), pp S4-S4
27 Nov 2007
PMID: 18047705
Abstract
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.
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Details
- Title
- A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method
- Creators
- Illhoi Yoo - Department of Health Management and Informatics, School of Medicine, University of Missouri-Columbia, USA. yooil@health.missouri.eduXiaohua HuIl-Yeol Song
- Publication Details
- BMC bioinformatics, v 8 Suppl 9(9), pp S4-S4
- Publisher
- Springer BMC; England
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:000259021300004
- Scopus ID
- 2-s2.0-38449085530
- Other Identifier
- 991014877832104721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Biochemical Research Methods
- Biotechnology & Applied Microbiology
- Mathematical & Computational Biology