论文标题
从时间个人健康数据中迈入神经数字到文本
Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
论文作者
论文摘要
随着人们对旨在跟踪用户数据（例如，营养摄入量，步骤计数）的个人健康技术的生产兴趣，现在有比以往任何时候都更多的机会以自然语言形式向日常用户表达有意义的行为见解。这些知识可以提高他们的行为意识，并允许他们采取行动以实现其健康目标。它还可以弥合大量个人健康数据的差距与描述个人行为趋势所需的摘要生成之间的差距。先前的工作重点是基于规则的时间序列数据摘要方法，旨在生成自然语言摘要，这些摘要是在时间个人健康数据中发现的有趣模式的自然语言摘要。我们检查了经常性，卷积和基于变压器的编码器模型，以自动从数字时间个人健康数据中生成自然语言摘要。我们展示了模型对MyFitnessPal记录的真实用户健康数据的有效性，并表明我们可以自动生成高质量的自然语言摘要。我们的工作是朝着从个人健康数据中自动产生新颖和有意义的时间摘要的雄心勃勃的目标的第一步。
With an increased interest in the production of personal health technologies designed to track user data (e.g., nutrient intake, step counts), there is now more opportunity than ever to surface meaningful behavioral insights to everyday users in the form of natural language. This knowledge can increase their behavioral awareness and allow them to take action to meet their health goals. It can also bridge the gap between the vast collection of personal health data and the summary generation required to describe an individual's behavioral tendencies. Previous work has focused on rule-based time-series data summarization methods designed to generate natural language summaries of interesting patterns found within temporal personal health data. We examine recurrent, convolutional, and Transformer-based encoder-decoder models to automatically generate natural language summaries from numeric temporal personal health data. We showcase the effectiveness of our models on real user health data logged in MyFitnessPal and show that we can automatically generate high-quality natural language summaries. Our work serves as a first step towards the ambitious goal of automatically generating novel and meaningful temporal summaries from personal health data.
