基于实体驱动对比学习的医学问题总结（英文版）

英文标题：Medical Question Summarization with Entity-driven Contrastive Learning中文摘要：该论文提出了一种基于实体驱动对比学习（ECL）的医学问题总结框架，以更准确地理解消费者意图和检索合适的答案，同时解决了数据泄露问题和正确捕捉语义意图的挑战。英文摘要：By summarizing longer consumer health questions into shorter and essentialones, medical question answering (MQA) systems can more accurately understandconsumer intentions and retrieve suitable answers. However, medical questionsummarization is very challenging due to obvious distinctions in health troubledescriptions from patients and