# Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

arXiv: https://arxiv.org/abs/2208.01780 · HTML: https://mlsanity.com/paper/2208.01780/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Aug 2022.

## Built on by (most cited first, 17 of 17)

- [UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoning](https://mlsanity.com/paper/2305.14761/) (2023)
- [VisText: A Benchmark for Semantically Rich Chart Captioning](https://mlsanity.com/paper/2307.05356/) (2023)
- [Graphologue: Exploring Large Language Model Responses with Interactive Diagrams](https://mlsanity.com/paper/2305.11473/) (2023)
- [ChartCap: Mitigating Hallucination of Dense Chart Captioning](https://mlsanity.com/paper/2508.03164/) (2025)
- [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)
- [SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings](https://mlsanity.com/paper/2403.17784/) (2024)
- [Grounded Intuition of GPT-Vision's Abilities with Scientific Images](https://mlsanity.com/paper/2311.02069/) (2023)
- [LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles](https://mlsanity.com/paper/2506.06561/) (2025)
- [From Charts to Fair Narratives: Uncovering and Mitigating Geo-Economic Biases in Chart-to-Text](https://mlsanity.com/paper/2508.09450/) (2025)
- [ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models](https://mlsanity.com/paper/2605.23694/) (2026)
- [An Image-based Typology for Visualization](https://mlsanity.com/paper/2403.05594/) (2024)
- [Capturing Visualization Design Rationale](https://mlsanity.com/paper/2506.16571/) (2025)
- [AnnoBench: A Benchmark for Visualization Annotation Generation](https://mlsanity.com/paper/2607.25911/) (2026)
- [Navigating the Rashomon Effect: How Personalization Can Help Adjust Interpretable Machine Learning Models to Individual Users](https://mlsanity.com/paper/2505.07100/) (2025)
- [ChartAnno: Benchmarking Multimodal Large Language Models for Chart Annotation Generation](https://mlsanity.com/paper/2608.03464/) (2026)
- [Same Chart, Different Story: Bias in Vision-Language Chart Interpretation](https://mlsanity.com/paper/2609.26210/) (2026)

## Abstract (arXiv)

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.