Generating coherent topic descriptions from the user comments of case-related topics plays a significant role in quickly understanding the case-related news, which can be regarded as a multi-document summarization task based on user comments. However, these comments contain lots of noise, the crucial information for generating summaries is scattered in different comments, the sequence-to-sequence model tends to generate irrelevant and incorrect summaries. Based on these observations, this study presents a case-related topic summarization method based on the topic interaction graph, which reconstructs the user comments into a topic interaction graph. The motivation is that the graph can express the correlation between different user comments, which is useful to filter the key information in user comments. Specifically, the case elements are first extracted from the user comments, and then the topic interaction graph is constructed, which takes the case elements as the nodes and uses the sentences including these case elements as the node’s contents; then the graph transformer network is introduced to produce the representation of the graph. Finally, the summary is generated by using a standard transformer-based decoder. The experimental results on the collected case-related topic summarization corpus show that the proposed method effectively selects useful content and can generate coherent and factual topic summaries.