During software development and maintenance, code comments often have some problems, such as missing, insufficient, or mismatching with code content. Writing high-quality code comments takes time and effort for developers, and the quality can not be guaranteed, therefore, it is urgent for researchers to design effective automatic code comment generation methods. The automatic code comment generation issue is an active research topic in the program comprehension domain. This study conducts a systematic review of this research topic. The existing methods are divided into three categories:Template-based generation methods, information retrieval-based methods, and deep learning-based methods. Related studies are analyzed and summarizedfor each category. Then, the corpora and comment quality evaluation methods that are often used in previous studiesare analyzed, which can facilitate the experimental study for future studies. Finally, the potential research directions in the future aresummarized and discussed.