Towards Efficient Medical Dialogue Summarization with Compacting-Abstractive Model

Abstract

Medical dialogue summarization (MDS) refers to automatically generating electronic health records (EHRs) from doctor-patient dialogues to relieve doctors from the recording burden. The long-term nature of medical dialogue makes it more efficient to handle MDS with a two-stage abstractive summarization model, which first extracts salient content from the source text and then generates an abstract summary based on that. However, this commonly used extractive-abstractive paradigm struggles to identify absolute salient statements in the first stage while discards all information mistakenly considered unimportant, heavily limiting the second-stage summarization performance. In this paper, we introduce a novel two-stage model for MDS with a compact-then-abstract workflow to ensure data efficiency and information integrity. After predicting EHR-related utterances in a medical dialogue, our model adaptively compresses the remaining into a soft context and generates an EHR according to both the prediction and compression results. This way, we loosen the traditional first-stage extraction to a hybrid of extraction and compression, which makes the input context compact and avoids suffering from losing information for producing an EHR. Extensive experiments on two public datasets show that the proposed model significantly outperforms the state-of-the-art counterparts w.r.t. multiple metrics.