Survey generation aims to generate a summary from a scientific topic based on related papers. The structure of papers deeply influences the generative process of survey, especially the relationships between sentence and sentence, paragraph and paragraph. In principle, the structure of paper can influence the quality of the summary. Therefore, we employ the structure of paper to leverage contextual information among sentences in paragraphs to generate a survey for documents. In particular, we present a neural document structure model for survey generation. We take paragraphs as units, and model sentences in paragraphs, we then employ a hierarchical model to learn structure among sentences, which can be used to select important and informative sentences to generate survey. We evaluate our model on scientific document data set. The experimental results show that our model is effective, and the generated survey is informative and readable.

调查生成的目的是根据相关论文从科学主题生成摘要。论文的结构深刻影响着调查的产生过程，尤其是句子与句子，段落与段落之间的关系。原则上，论文的结构会影响摘要的质量。因此，我们采用纸张结构来利用段落中句子之间的上下文信息来生成文档调查。特别是，我们提出了用于调查生成的神经文档结构模型。我们以段落为单位，在段落中对句子进行建模，然后采用分层模型来学习句子之间的结构，该模型可用于选择重要且内容丰富的句子以进行调查。我们根据科学文献数据集评估我们的模型。