Relation extraction (RE) is one of the most important tasks in information extraction of NLP, the result of RE can be used to downstream missions such as construction of knowledge graphs, knowledge base question answering, semantic search et al. which means RE has wide-ranging application scenarios and important research value. Recent years, RE achieves frutiful results, but most of them are limited in sentence-level RE, which focus on extract relation between two mentions within a single sentence. Reserches shows that a large number of relations can’t extract from a single sentence, in rencent years, document-level RE faces new opportunities and challenges with the development of deep learning and NLP. This study reviews the recent advances in document-level RE research, summarize a general technology roadmap of this task, and then analyzes the encoding and aggregation methods used in the researches, We also introduce the common datasets and evaluation metrics of this task. This paper ends up with forecasting the future development trend of this task.