Medical dialogue generation is an important yet challenging task. Most
previous works rely on the attention mechanism and large-scale pretrained
language models. However, these methods often fail to acquire pivotal
information from the long dialogue history to yield an accurate and informative
response, due to the fact that the medical entities usually scatters throughout
multiple utterances along with the complex relationships between them. To
mitigate this problem, we propose a medical response generation model with
Pivotal Information Recalling (MedPIR), which is built on two components, i.e.,
knowledge-aware dialogue graph encoder and recall-enhanced generator. The
knowledge-aware dialogue graph encoder constructs a dialogue graph by
exploiting the knowledge relationships between entities in the utterances, and
encodes it with a graph attention network. Then, the recall-enhanced generator
strengthens the usage of these pivotal information by generating a summary of
the dialogue before producing the actual response. Experimental results on two
large-scale medical dialogue datasets show that MedPIR outperforms the strong
baselines in BLEU scores and medical entities F1 measure.

提出一种基于关键信息回顾技术的医学响应生成模型，该模型通过利用知识关系构建对话图并加以编码，以及生成对话前的总结以加强关键信息的利用，取得了较强的表现。