Summarization of doctor-patient conversations into clinical notes by medical scribes is an essential process for effective clinical care. Pre-trained transformer models have shown a great amount of success in this area, but the domain shift from standard NLP tasks to the medical domain continues to present challenges. We build upon several recent works to show that additional pre-training with in-domain medical conversations leads to performance gains for clinical summarization. In addition to conventional evaluation metrics, we also explore a clinical named entity recognition model for concept-based evaluation. Finally, we contrast long-sequence transformers with a common transformer model, BART. Overall, our findings corroborate research in non-medical domains and suggest that in-domain pre-training combined with transformers for long sequences are effective strategies for summarizing clinical encounters.

Fine-tuning pretrained models for automatically summarizing doctor-patient conversation transcripts presents many challenges: limited training data, significant domain shift, long and noisy transcripts, and high target summary variability. In this paper, we explore the feasibility of using pretrained transformer models for automatically summarizing doctor-patient conversations directly from transcripts. We show that fluent and adequate summaries can be generated with limited training data by fine-tuning BART on a specially constructed dataset. The resulting models greatly surpass the performance of an average human annotator and the quality of previous published work for the task. We evaluate multiple methods for handling long conversations, comparing them to the obvious baseline of truncating the conversation to fit the pretrained model length limit. We introduce a multistage approach that tackles the task by learning two fine-tuned models: one for summarizing conversation chunks into partial summaries, followed by one for rewriting the collection of partial summaries into a complete summary. Using a carefully chosen fine-tuning dataset, this method is shown to be effective at handling longer conversations, improving the quality of generated summaries. We conduct both an automatic evaluation (through ROUGE and two concept-based metrics focusing on medical findings) and a human evaluation (through qualitative examples from literature, assessing hallucination, generalization, fluency, and general quality of the generated summaries).