基于Clique聚类的精神分裂症多文档自动摘要研究

利用SemRep将精神分裂文献集处理为语义述谓项集，建立语义述谓网络，从节点、边及网络凝聚性三个层次提取文献集的核心内容，生成的图形摘要由具有高凝聚性的clique组成，探索利用网络属性结合语义信息的生物医学多文档自动摘要方法。通过clique共节点矩阵对其聚类获取摘要的子主题，采用人工标准对摘要内容的覆盖面进行评价，结果显示摘要的准确率为0.93，召回率为0.68，F值为0.79。该方法能有效识别文献集中的核心内容，网络图中所富含的语义信息能较完整地表达摘要内容。

Clique clustering-based automatic summarization of multiple documents on schizophrenia

A semantic predication network was developed by processing the documents on schizophrenia into semantic predication sets using SemRep, from which the core information was extracted to produce a graphic summary which was consisted of highly cohesive cliques. The automatic methods for summarizing biomedical documents were studied using the network properties combined with semantic information. The subthemes in the summary obtained by clustering were evaluated according to the clique co-node matrix and the contents of the summary were assessed according to the reference criteria. The accuracy was 0.93, the recall was 0.68, and the F-value was 0.79 for the summary, indicating that this method can effectively recognize the core information in documents and the semantic information in network graphics.