Generating a Natural Language Sentence explaining the Trends of Two Time-Series Data

Abstract

Most data-to-text studies use numerical data to generate natural language sentences to describe events in the target domain, but few natural language sentence generation methods have been developed to capture the analytical meaning of numerical data or relationships among multiple numerical data. In this study, we propose a natural language sentence generation method that can capture the relationship between two time-series data sets and various relationships regarding their trends. We created two time-series data sets and experimented with them, and found that the evaluation data could be reproduced with considerable accuracy in the generated natural language sentences.