Towards Neural Numeric-To-Text Generation From Temporal Personal Health Data
This work addresses the need to provide meaningful behavioral insights to everyday users from their tracked health data, bridging the gap between data collection and summary generation, though it is incremental as it builds on existing neural methods for text generation.
The paper tackled the problem of automatically generating natural language summaries from numeric temporal personal health data, such as nutrient intake and step counts, using neural encoder-decoder models, and demonstrated effectiveness on real user data from MyFitnessPal with high-quality results.
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.