A coherent graph-based semantic clustering and summarization approach for biomedical literature and a new summarization evaluation method.

ABSTRACT:

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.

Conclusion

Our

SUBMITTER: Yoo I 

PROVIDER: S-EPMC2217662 | biostudies-literature | 2007 Nov

REPOSITORIES: biostudies-literature