Kori: Interactive Synthesis of Text and Charts in Data Documents
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Abstract (arXiv)
Charts go hand in hand with text to communicate complex data and are widely adopted in news articles, online blogs, and academic papers. They provide graphical summaries of the data, while text explains the message and context. However, synthesizing information across text and charts is difficult; it requires readers to frequently shift their attention. We investigated ways to support the tight coupling of text and charts in data documents. To understand their interplay, we analyzed the design space of chart-text references through news articles and scientific papers. Informed by the analysis, we developed a mixed-initiative interface enabling users to construct interactive references between text and charts. It leverages natural language processing to automatically suggest references as well as allows users to manually construct other references effortlessly. A user study complemented with algorithmic evaluation of the system suggests that the interface provides an effective way to compose interactive data documents.
Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). First posted on arXiv 9 Aug 2021.
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Citing papers per year: 2023: 4; 2024: 3; 2025: 2; 2026: 1.
10 papers.
- 2023VisText: A Benchmark for Semantically Rich Chart Captioningcited by 111
- 2023DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articlescited by 24
- 2024Generative AI for Visualization: State of the Art and Future Directionscited by 23
- 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaborationcited by 13
- 2023The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfacescited by 9
- 2024Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagramscited by 6
- 2024Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directionscited by 5
- 2025DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Textcited by 4
- 2025Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answerscited by 3
- 2026ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Modelscited by 2
- 2026ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models22 May 2026
- 2025Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers1 Oct 2025
- 2025DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text29 Mar 2025
- 2024Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions29 Sep 2024
- 2024Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams28 May 2024
- 2024Generative AI for Visualization: State of the Art and Future Directions28 Apr 2024
- 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration27 Sep 2023
- 2023DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles8 Aug 2023
- 2023VisText: A Benchmark for Semantically Rich Chart Captioning28 Jun 2023
- 2023The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces25 Mar 2023