Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model

TL;DR

This paper introduces a neural transformer-based model and dataset for automatically generating natural language summaries of charts, aiding understanding especially for visually impaired users.

Contribution

It presents a new dataset and extends a transformer model for chart-to-text generation, significantly improving content selection and summary quality.

Findings

Outperforms baseline model on content selection metric (55.42% vs. 8.49%)

Generates more informative and coherent summaries

Enhances accessibility for visually impaired users

Abstract

Information visualizations such as bar charts and line charts are very popular for exploring data and communicating insights. Interpreting and making sense of such visualizations can be challenging for some people, such as those who are visually impaired or have low visualization literacy. In this work, we introduce a new dataset and present a neural model for automatically generating natural language summaries for charts. The generated summaries provide an interpretation of the chart and convey the key insights found within that chart. Our neural model is developed by extending the state-of-the-art model for the data-to-text generation task, which utilizes a transformer-based encoder-decoder architecture. We found that our approach outperforms the base model on a content selection metric by a wide margin (55.42% vs. 8.49%) and generates more informative, concise, and coherent summaries.