Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts
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Abstract (arXiv)
Charts often contain visually prominent features that draw attention to aspects of the data and include text captions that emphasize aspects of the data. Through a crowdsourced study, we explore how readers gather takeaways when considering charts and captions together. We first ask participants to mark visually prominent regions in a set of line charts. We then generate text captions based on the prominent features and ask participants to report their takeaways after observing chart-caption pairs. We find that when both the chart and caption describe a high-prominence feature, readers treat the doubly emphasized high-prominence feature as the takeaway; when the caption describes a low-prominence chart feature, readers rely on the chart and report a higher-prominence feature as the takeaway. We also find that external information that provides context, helps further convey the caption's message to the reader. We use these findings to provide guidelines for authoring effective chart-caption pairs.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jan 2021.
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Citing papers per year: 2021: 2; 2022: 3; 2023: 1; 2024: 3; 2025: 2; 2026: 1.
12 papers.
- 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoningcited by 1,449
- 2022Chart-to-Text: A Large-Scale Benchmark for Chart Summarizationcited by 166
- 2023VisText: A Benchmark for Semantically Rich Chart Captioningcited by 111
- 2021Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Contentcited by 38
- 2022LineCap: Line Charts for Data Visualization Captioning Modelscited by 17
- 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Modelscited by 12
- 2025ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Datasetcited by 9
- 2024C2: Scalable Auto-Feedback for LLM-based Chart Generationcited by 6
- 2024SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratingscited by 5
- 2025LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profilescited by 2
- 2026ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Modelscited by 2
- 2024Visualization of Age Distributions as Elements of Medical Data-Storiescited by 0
- 2026ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models22 May 2026
- 2025LaMP-Cap: Personalized Figure Caption Generation With Multimodal Figure Profiles6 Jun 2025
- 2025ChartInsighter: An Approach for Mitigating Hallucination in Time-series Chart Summary Generation with A Benchmark Dataset16 Jan 2025
- 2024C2: Scalable Auto-Feedback for LLM-based Chart Generation24 Oct 2024
- 2024Visualization of Age Distributions as Elements of Medical Data-Stories26 Sep 2024
- 2024SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings26 Mar 2024
- 2023VisText: A Benchmark for Semantically Rich Chart Captioning28 Jun 2023
- 2022LineCap: Line Charts for Data Visualization Captioning Models15 Jul 2022
- 2022ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning19 Mar 2022
- 2022Chart-to-Text: A Large-Scale Benchmark for Chart Summarization12 Mar 2022
- 2021Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content8 Oct 2021
- 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models4 Aug 2021