  Many conversation datasets have been constructed in the recent years using
crowdsourcing. However, the data collection process can be time consuming and
presents many challenges to ensure data quality. 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 as the input. We explore
three approaches to generate summary grounded conversations, and evaluate the
generated conversations using automatic measures and human judgements. We also
show that the accuracy of conversation summarization can be improved by
augmenting a conversation summarization dataset with generated conversations.
