Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
This work addresses the challenge of interpreting charts for people with visual impairments or low visualization literacy, though it is incremental as it adapts an existing transformer model.
The paper tackles the problem of automatically generating natural language summaries for charts to aid interpretation, and the result is a neural model that outperforms a base model by a wide margin (55.42% vs. 8.49%) in content selection.
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.