Many conversation datasets have been constructed in the recent years using crowd-sourcing. However, the data collection process can be time consuming and presents many challenges. Since language generation has improved immensely in recent years with the advancement of pre-trained language models, we investigate how such models can be utilized to generate entire conversations given only a summary of a conversation. We explore three approaches to generate summary grounded conversations, and evaluate the generated conversations using automatic measures and human judgements. We show that the generated conversations can be used to improve the performance of the conversation summarization task.