  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.
