Abstract: A system automatically display a summary of a customer-agent interaction before the end of the interaction. The system includes an agent computing device in communication with a contact center control system. Over a period of time, the system receives a group of interactions from a number of customers, and trains a machine learning model to assign an end-of-call probability weight to each phrase in the interactions. The system then receives a real-time interaction including a number of utterances, connects the interaction to the agent computing device, constructs a real-time transcript and, with the machine learning model, computes a cumulative end-of-call probability based on the utterances. When the end-of-call probability exceeds a threshold value the system constructs or updates a summary of the transcript, displays the summary, and performs at least one automated action based on the contents of the summary.