[Purpose/significance] A wide variety of topic models has been developed with improved algorithm. This paper aims to study the research advances, generation process and algorithm of citation based topic models. Additionally, we discuss the application in the text of academic articles and research areas in the future.[Method/process] Based on the data of Web of Science and CNKI database, we collected articles of citation based topic models. In these articles, we selected several representative articles after manual interpretation to analyze the generative process, parameter estimation and inference methods in these citation based topic models.[Result/conclusion] Currently, there are mainly three types of citation based topic models. This includes the topic models which focus on the topic-citation distribution, while other topic models mainly study the relationship between the citing documents and the cited documents. Besides, citation context based topic models are also available. Additionally, more complete topic content can be detected after introducing citation information into the topic models. Moreover, most of the models are the variants of LDA and PLSA. In future, incorporating citation context information into topic models, improving the inference methods and applying the models are some of the future directions.
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