AutoChart: A Dataset for Chart-to-Text Generation Task

TL;DR

AutoChart introduces a large dataset for chart-to-text generation, enabling research into automatic analytical descriptions of charts with a novel framework that generates charts and descriptions automatically.

Contribution

The paper presents AutoChart, a new dataset and a framework for automatic chart generation and description, addressing a limited area in computational linguistics.

Findings

Generated descriptions are informative and coherent.

Extensive human and machine evaluations validate the quality.

Framework effectively links charts with analytical descriptions.

Abstract

The analytical description of charts is an exciting and important research area with many applications in academia and industry. Yet, this challenging task has received limited attention from the computational linguistics research community. This paper proposes \textsf{AutoChart}, a large dataset for the analytical description of charts, which aims to encourage more research into this important area. Specifically, we offer a novel framework that generates the charts and their analytical description automatically. We conducted extensive human and machine evaluations on the generated charts and descriptions and demonstrate that the generated texts are informative, coherent, and relevant to the corresponding charts.