Machine translation (MT) aims to build an automatic translating system to transform a given sequence in the source language into another target language sequence that shares identical semantic information. MT has been an important research direction in natural language processing and artificial intelligence fields for its widely applied scenarios. In recent years, the performance of neural machine translation (NMT) greatly surpasses that of statistical machine translation (SMT), becoming the mainstream method in MT research. However, NMT generally takes the sentence as the translated unit, and in document-level translation scenarios, some discourse errors such as the mistranslation of words and incoherent sentences may occur due to the separation with discourse context if the sentence is translated independently. Therefore, incorporating document-level information into the procedure of translation may be a more reasonable and natural way to solve discourse errors. This conforms with the goal of document-level neural machine translation (DNMT) and has been a popular direction in MT research. This study reviews and summarizes works in DNMT research in terms of discourse evaluation methods, datasets and models applied, and other aspects to help the researchers efficiently learn the research status and further directions of DNMT. Meanwhile, this study also introduces the prospect and some challenges in DNMT, hoping to bring some inspiration to researchers.