A Factual Aware Two-Stage Model for Medical Dialogue Summarization

Medical dialogue summarization (MDS) is commonly known as generating electronic health records (EHR) from doctor-patient dialogues to relieve doctors from trivial recordings. Because of their excellent performance on summarization tasks, it is advisable to employ pre-trained language models (PLM) for MDS. However, most of these models are not designed to handle such lengthy dialogues and struggle with domain-specific characteristics. To address this problem, we propose a two-stage summarization model that first constructs compact contexts by selecting salient utterances and then generates EHRs with delexicalization and lexicalization. A REINFORCE algorithm with a multiple reward strategy is employed to connect the two modules to increase faithfulness and adaptively control the length of the extracted context. We implement our model using publicly available PLMs without changing their nature, and we pre-train each module separately before alternately fine-tuning them with the reinforcement objective. Extensive experiments on two public datasets show that our proposed model significantly outperforms state-of-the-art comparison models w.r.t. ROUGE score and terminology matching rate.