An Explorative System for Large-scale Table Data That Combines Data Visualization and Text Generation Techniques

Abstract

In recent years, data analysis systems that combine natural language generation and visualization are increasing. The medium of text is superior to visualization in that it does not require special knowledge for users to understand the important facts buried in data. However, automatically summarizing large data with texts becomes too long, because in table data, the more attributes there are, the more statistical features there are. In this study, we address this scalability problem by using the hierarchical structure that many large data sets have explicitly or implicitly, in both items and attributes. First, the system focuses text generation and visualization only on the areas that users are interested in. Secondly, it interactively shifts the focus as the user's interest shifts. In this way, we propose an idea for a system that allows users to explore the entire data smoothly while limiting the amount of text presented at once. We also implement the system and show that this idea is effective.