# LineCap: Line Charts for Data Visualization Captioning Models

arXiv: https://arxiv.org/abs/2207.07243 · HTML: https://mlsanity.com/paper/2207.07243/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 37 references that are in the map.
- First posted on arXiv 15 Jul 2022.

## Builds on (most cited first, 18 of 18)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Models](https://mlsanity.com/paper/1505.04870/) (2015)
- [BLEURT: Learning Robust Metrics for Text Generation](https://mlsanity.com/paper/2004.04696/) (2020)
- [DVQA: Understanding Data Visualizations via Question Answering](https://mlsanity.com/paper/1801.08163/) (2018)
- [FigureQA: An Annotated Figure Dataset for Visual Reasoning](https://mlsanity.com/paper/1710.07300/) (2017)
- [ToTTo: A Controlled Table-To-Text Generation Dataset](https://mlsanity.com/paper/2004.14373/) (2020)
- [Chart-to-Text: A Large-Scale Benchmark for Chart Summarization](https://mlsanity.com/paper/2203.06486/) (2022)
- [Captioning Images Taken by People Who Are Blind](https://mlsanity.com/paper/2002.08565/) (2020)
- [Logical Natural Language Generation from Open-Domain Tables](https://mlsanity.com/paper/2004.10404/) (2020)
- [Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Model](https://mlsanity.com/paper/2010.09142/) (2020)
- [SciCap: Generating Captions for Scientific Figures](https://mlsanity.com/paper/2110.11624/) (2021)
- [Re-evaluating Automatic Metrics for Image Captioning](https://mlsanity.com/paper/1612.07600/) (2016)
- [Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Content](https://mlsanity.com/paper/2110.04406/) (2021)
- [Answering Questions about Data Visualizations using Efficient Bimodal Fusion](https://mlsanity.com/paper/1908.01801/) (2019)
- [Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Charts](https://mlsanity.com/paper/2101.08235/) (2021)
- [Truth-Conditional Captioning of Time Series Data](https://mlsanity.com/paper/2110.01839/) (2021)

## 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)
- [From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models](https://mlsanity.com/paper/2403.12027/) (2024)
- [ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rules](https://mlsanity.com/paper/2304.02173/) (2023)
- [Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarization](https://mlsanity.com/paper/2302.12324/) (2023)
- [Time Series Language Model for Descriptive Caption Generation](https://mlsanity.com/paper/2501.01832/) (2025)
- [PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figures](https://mlsanity.com/paper/2501.15074/) (2025)
- [ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answering](https://mlsanity.com/paper/2505.23242/) (2025)
- [DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Framework](https://mlsanity.com/paper/2507.21830/) (2025)
- [Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Data](https://mlsanity.com/paper/2409.16647/) (2024)
- [Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directions](https://mlsanity.com/paper/2409.19747/) (2024)
- [SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings](https://mlsanity.com/paper/2403.17784/) (2024)
- [Generalization of CNNs on Relational Reasoning with Bar Charts](https://mlsanity.com/paper/2503.00086/) (2025)
- [TADACap: Time-series Adaptive Domain-Aware Captioning](https://mlsanity.com/paper/2504.11441/) (2025)
- [ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Models](https://mlsanity.com/paper/2605.23694/) (2026)
- [DiffNator: Generating Structured Explanations of Time-Series Differences](https://mlsanity.com/paper/2509.20007/) (2025)
- [Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Tasks](https://mlsanity.com/paper/2510.06071/) (2025)

## Abstract (arXiv)

Data visualization captions help readers understand the purpose of a visualization and are crucial for individuals with visual impairments. The prevalence of poor figure captions and the successful application of deep learning approaches to image captioning motivate the use of similar techniques for automated figure captioning. However, research in this field has been stunted by the lack of suitable datasets. We introduce LineCap, a novel figure captioning dataset of 3,528 figures, and we provide insights from curating this dataset and using end-to-end deep learning models for automated figure captioning.