Massive scientific and technical literature has recorded the developments of science and technology and contains plentiful knowledge. Researchers have to read scientific literature such as papers, patents, and reports to know the latest developments in time. However, it is difficult for researchers to read all the newly published and relevant literature. So there is an urgent need for scientific literature summarization systems to provide brief and important dynamic information that researchers are interested in. This paper proposes an approach to generate automatic summarization based on 5W1H event structure. Sentences in the literature are classified and selected for different elements of events by relevance, and then the importance of each candidate sentence is calculated. Top-k relevant and important sentences are selected to formulate event-based summarization. Comparing with existing summarization results or abstracts given by authors, experiment results of our approach contain more detailed information with the the 5W1H event structure, which is more convenient for researchers to search and browse the brief description of scientific and technical information distributed in massive scientific literature.

大量的科学技术文献记录了科学技术的发展，并包含丰富的知识。研究人员必须阅读科学文献，例如论文，专利和报告，才能及时了解最新动态。但是，研究人员很难阅读所有新出版的相关文献。因此，迫切需要科学文献摘要系统提供研究人员感兴趣的简短重要动态信息。本文提出了一种基于5W1H事件结构的自动摘要生成方法。根据相关性对文献中的句子进行分类并针对事件的不同元素进行选择，然后计算每个候选句子的重要性。前k选择相关和重要的句子以制定基于事件的摘要。与现有的摘要结果或作者给出的摘要相比，我们的方法的实验结果包含具有5W1H事件结构的更详细的信息，从而更便于研究人员搜索和浏览在大量科学文献中分发的科学技术信息的简要说明。 。