：科学论文篇章结构解析是规范科学论文写作、理解其内容、快速定位和抽取论文中特定信息的前提与基础。文章采用文献调查法和对比分析法，从篇章结构建模、篇章结构自动解析、篇章结构应用三个方面对相关文献进行梳理和总结。研究结果发现当前针对科学论文篇章结构的研究主要集中在生物医学和计算语言学领域，以粗粒度的基于修辞结构的篇章模型为主，自动解析主要采用文本分类和序列标注两大类方法，在自动文摘、基于上下文的引文分析等任务中都有重要的应用。今后研究应扩展到其他领域，并聚焦基于修辞和论证结构的细粒度篇章结构建模，采用深度学习技术实现更精确的篇章结构解析。

Discourse parsing of scientific articles is the premise and basis for standardizing the writing of scientific articles, understanding their content, and quickly locating and extracting specific information from them. This paper analyzes and summarizes related literature from three aspects: discourse structure modeling, discourse parsing and their applications by literature survey and comparative analysis. The results show that the current research focuses on the coarse-grained models of discourse structure in the domains of bio-medicine and computational linguistics. Automatic discourse parsing mainly adopts two kinds of methods: text classification and sequence labeling. Discourse structure modelling and discourse parsing has important applications in many tasks such as automatic summarization and context-based citation analysis. Future research should be extended to other domains, pay more attention to fine-grained discourse structure models based on rhetoric and argumentation structure, and apply deep learning techniques to achieve more accurate discourse parsing.