# ChartStory: Automated Partitioning, Layout, and Captioning of Charts into Comic-Style Narratives

arXiv: https://arxiv.org/abs/2103.03996 · HTML: https://mlsanity.com/paper/2103.03996/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2021.

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

- [Chart Question Answering: State of the Art and Future Directions](https://mlsanity.com/paper/2205.03966/) (2022)
- [Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaboration](https://mlsanity.com/paper/2309.15723/) (2023)
- [One missing piece in Vision and Language: A Survey on Comics Understanding](https://mlsanity.com/paper/2409.09502/) (2024)
- [Data Analysis in the Era of Generative AI](https://mlsanity.com/paper/2409.18475/) (2024)
- [Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions](https://mlsanity.com/paper/2409.19747/) (2024)
- [C2: Scalable Auto-Feedback for LLM-based Chart Generation](https://mlsanity.com/paper/2410.18652/) (2024)
- [MDSF: Context-Aware Multi-Dimensional Data Storytelling Framework based on Large language Model](https://mlsanity.com/paper/2501.01014/) (2025)
- [DashChat: Interactive Authoring of Performance Dashboard Design Prototypes through Conversation with LLM-Powered Agent](https://mlsanity.com/paper/2504.12865/) (2025)
- [Evaluating Graphical Perception Capabilities of Vision Transformers](https://mlsanity.com/paper/2602.18178/) (2026)
- [DataScout: Automatic Data Fact Retrieval for Statement Augmentation with an LLM-Based Agent](https://mlsanity.com/paper/2504.17334/) (2025)

## Abstract (arXiv)

Visual data storytelling is gaining importance as a means of presenting data-driven information or analysis results, especially to the general public. This has resulted in design principles being proposed for data-driven storytelling, and new authoring tools being created to aid such storytelling. However, data analysts typically lack sufficient background in design and storytelling to make effective use of these principles and authoring tools. To assist this process, we present ChartStory for crafting data stories from a collection of user-created charts, using a style akin to comic panels to imply the underlying sequence and logic of data-driven narratives. Our approach is to operationalize established design principles into an advanced pipeline which characterizes charts by their properties and similarity, and recommends ways to partition, layout, and caption story pieces to serve a narrative. ChartStory also augments this pipeline with intuitive user interactions for visual refinement of generated data comics. We extensively and holistically evaluate ChartStory via a trio of studies. We first assess how the tool supports data comic creation in comparison to a manual baseline tool. Data comics from this study are subsequently compared and evaluated to ChartStory's automated recommendations by a team of narrative visualization practitioners. This is followed by a pair of interview studies with data scientists using their own datasets and charts who provide an additional assessment of the system. We find that ChartStory provides cogent recommendations for narrative generation, resulting in data comics that compare favorably to manually-created ones.