期刊
PLOS ONE
卷 6, 期 8, 页码 -出版社
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0023862
关键词
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资金
- Natural Science Foundation of China [60673039, 61070098]
- National High Tech Research and Development Plan of China [2006AA01Z151]
- Doctoral Program Foundation of Institutions of Higher Education of China [20090041110002]
- Fundamental Research Funds for the Central Universities [DUT10JS09]
- Liaoning Province Doctor Startup Fund [20091015]
Automatic text summarization for a biomedical concept can help researchers to get the key points of a certain topic from large amount of biomedical literature efficiently. In this paper, we present a method for generating text summary for a given biomedical concept, e.g., H1N1 disease, from multiple documents based on semantic relation extraction. Our approach includes three stages: 1) We extract semantic relations in each sentence using the semantic knowledge representation tool SemRep. 2) We develop a relation-level retrieval method to select the relations most relevant to each query concept and visualize them in a graphic representation. 3) For relations in the relevant set, we extract informative sentences that can interpret them from the document collection to generate text summary using an information retrieval based method. Our major focus in this work is to investigate the contribution of semantic relation extraction to the task of biomedical text summarization. The experimental results on summarization for a set of diseases show that the introduction of semantic knowledge improves the performance and our results are better than the MEAD system, a well-known tool for text summarization.
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