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Information visualizations such as bar charts and line charts are verypopular for exploring data and communicating insights. Interpreting and makingsense of such visualizations can be challenging for some people, such as thosewho are visually impaired or have low visualization literacy. In this work, weintroduce a new dataset and present a neural model for automatically generatingnatural language summaries for charts. The generated summaries provide aninterpretation of the chart and convey the key insights found within thatchart. Our neural model is developed by extending the state-of-the-art modelfor the data-to-text generation task, which utilizes a transformer-basedencoder-decoder architecture. We found that our approach outperforms the basemodel on a content selection metric by a wide margin (55.42% vs. 8.49%) andgenerates more informative, concise, and coherent summaries.

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