论文标题
部分可观测时空混沌系统的无模型预测
CHQ-Summ: A Dataset for Consumer Healthcare Question Summarization
论文作者
论文摘要
寻求健康信息的寻求与消费者与健康相关的问题淹没了网络。通常，消费者使用过度描述性和外围信息来表达其医疗状况或其他医疗保健需求，从而有助于自然语言理解的挑战。解决这一挑战的一种方法是总结问题并提取原始问题的关键信息。为了解决此问题，我们介绍了一个新的数据集，CHQ-SUMM包含1507个域 - 专家注释的消费者健康问题和相应的摘要。该数据集源自社区提问论坛，因此为了解社交媒体上与消费者健康相关的帖子提供了宝贵的资源。我们在多个最先进的摘要模型上基准测试数据集，以显示数据集的有效性。
The quest for seeking health information has swamped the web with consumers' health-related questions. Generally, consumers use overly descriptive and peripheral information to express their medical condition or other healthcare needs, contributing to the challenges of natural language understanding. One way to address this challenge is to summarize the questions and distill the key information of the original question. To address this issue, we introduce a new dataset, CHQ-Summ that contains 1507 domain-expert annotated consumer health questions and corresponding summaries. The dataset is derived from the community question-answering forum and therefore provides a valuable resource for understanding consumer health-related posts on social media. We benchmark the dataset on multiple state-of-the-art summarization models to show the effectiveness of the dataset.
