Text-based dialogues are now widely used to solve real-world problems. In cases where solution strategies are already known, they can sometimes be codified into workflows and used to guide humans or artificial agents through the task of helping clients. We are interested in the situation where a formal workflow may not yet exist, but we wish to discover the steps of actions that have been taken to resolve problems. We examine a novel transformer-based approach for this situation and we present experiments where we summarize dialogues in the Action-Based Conversations Dataset (ABCD) with workflows. Since the ABCD dialogues were generated using known workflows to guide agents we can evaluate our ability to extract such workflows using ground truth sequences of action steps, organized as workflows. We propose and evaluate an approach that conditions models on the set of allowable action steps and we show that using this strategy we can improve workflow discovery (WD) performance. Our conditioning approach also improves zero-shot and few-shot WD performance when transferring learned models to entirely new domains (i.e. the MultiWOZ setting). Further, a modified variant of our architecture achieves state-of-the-art performance on the related but different problems of Action State Tracking (AST) and Cascading Dialogue Success (CDS) on the ABCD.