# Kori: Interactive Synthesis of Text and Charts in Data Documents

arXiv: https://arxiv.org/abs/2108.04203 · HTML: https://mlsanity.com/paper/2108.04203/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2021.

## Built on by (most cited first, 10 of 10)

- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [DataTales: Investigating the use of Large Language Models for Authoring Data-Driven Articles](https://mlsanity.com/paper/2308.04076/) (2023)
- [Generative AI for Visualization: State of the Art and Future Directions](https://mlsanity.com/paper/2404.18144/) (2024)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [The Semantic Reader Project: Augmenting Scholarly Documents through AI-Powered Interactive Reading Interfaces](https://mlsanity.com/paper/2303.14334/) (2023)
- [Augmented Physics: Creating Interactive and Embedded Physics Simulations from Static Textbook Diagrams](https://mlsanity.com/paper/2405.18614/) (2024)
- [Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions](https://mlsanity.com/paper/2409.19747/) (2024)
- [DATAWEAVER: Authoring Data-Driven Narratives through the Integrated Composition of Visualization and Text](https://mlsanity.com/paper/2503.22946/) (2025)
- [Attribution Gradients: Incrementally Unfolding Citations for Critical Examination of Attributed AI Answers](https://mlsanity.com/paper/2510.00361/) (2025)
- [ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models](https://mlsanity.com/paper/2605.23694/) (2026)

## 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.