[Purpose/significance] Addressing the special features of medical text, this paper proposed a method for multidocument automatic summarization based on semantic graph. By taking advantage of the semantics in the graph, it identified the themes in the summary.[Method/process] SemRep was used to extract the standard concepts and semantic relations from medical documents, which then were used to construct the semantic graph. Subsequently, core concept, semantic relations and communities were sequentially extracted from the graph to compose the summary, and the mappings between concepts and semantic types as well as between semantic types and semantic groups were used to group concepts macroscopically. Schemas were defined to identify the themes in the summary.[Result/conclusion] Five datasets on diseases were used for testing and the results showed the method could effectively extract the core content from the documents. The semantics enriched in the graph could be used to precisely recognize the themes for the summary.
Zhang Han
,
Zhao Yuhong
. Constructing Semantic Graph Based on Summary Extracting Model for Multiple Medical Documents[J]. Library and Information Service, 2017
, 61(8)
: 112
-119
.
DOI: 10.13266/j.issn.0252-3116.2017.08.014
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