  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.
