School of Computer Science and Technology, Dalian University of Technology, Dalian, Liaoning, China.
PLoS One. 2011;6(8):e23862. doi: 10.1371/journal.pone.0023862. Epub 2011 Aug 26.
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.
生物医学概念的自动文本摘要可以帮助研究人员从大量生物医学文献中高效地获取某个主题的关键点。在本文中，我们提出了一种基于语义关系抽取的方法，从多个文档中为给定的生物医学概念（例如 H1N1 疾病）生成文本摘要。我们的方法包括三个阶段：1）使用语义知识表示工具 SemRep 从每个句子中提取语义关系。2）我们开发了一种关系级别的检索方法，选择与每个查询概念最相关的关系，并以图形表示的形式可视化它们。3）对于相关集中的关系，我们从文档集合中提取可以解释它们的信息性句子，使用基于信息检索的方法生成文本摘要。我们在这项工作中的主要重点是研究语义关系抽取对生物医学文本摘要任务的贡献。在疾病摘要方面的实验结果表明，引入语义知识可以提高性能，并且我们的结果优于 MEAD 系统，这是一个著名的文本摘要工具。
