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        <title>Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model
%A Obeid, Jason
%A Hoque, Enamul
%Y Davis, Brian
%Y Graham, Yvette
%Y Kelleher, John
%Y Sripada, Yaji
%S Proceedings of the 13th International Conference on Natural Language Generation
%D 2020
%8 December
%I Association for Computational Linguistics
%C Dublin, Ireland
%F obeid-hoque-2020-chart
%X 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.
%R 10.18653/v1/2020.inlg-1.20
%U https://aclanthology.org/2020.inlg-1.20
%U https://doi.org/10.18653/v1/2020.inlg-1.20
%P 138-147

Markdown (Informal)

[Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model](https://aclanthology.org/2020.inlg-1.20) (Obeid & Hoque, INLG 2020)

• Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model (Obeid & Hoque, INLG 2020)

ACL

• Jason Obeid and Enamul Hoque. 2020. Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model. In Proceedings of the 13th International Conference on Natural Language Generation, pages 138–147, Dublin, Ireland. Association for Computational Linguistics.