mlsanityData to 14 Oct 2026

LineCap: Line Charts for Data Visualization Captioning Models

arXiv 2207.07243 ↗First posted 15 Jul 2022cs.CV cs.CL cs.LGcited by 17

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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.

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

18 of 37 references.

References · 18

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,608
  2. 2016Densely Connected Convolutional Networkscited by 6,720
  3. 2014CIDEr: Consensus-based Image Description Evaluationcited by 2,046
  4. 2015Flickr30k Entities: Collecting Region-to-Phrase Correspondences for Richer Image-to-Sentence Modelscited by 1,362
  5. 2020BLEURT: Learning Robust Metrics for Text Generationcited by 1,203
  6. 2018DVQA: Understanding Data Visualizations via Question Answeringcited by 340
  7. 2017FigureQA: An Annotated Figure Dataset for Visual Reasoningcited by 319
  8. 2020ToTTo: A Controlled Table-To-Text Generation Datasetcited by 268
  9. 2022Chart-to-Text: A Large-Scale Benchmark for Chart Summarizationcited by 169
  10. 2020Captioning Images Taken by People Who Are Blindcited by 110
  11. 2020Logical Natural Language Generation from Open-Domain Tablescited by 90
  12. 2020Chart-to-Text: Generating Natural Language Descriptions for Charts by Adapting the Transformer Modelcited by 78
6 more
  1. 2021SciCap: Generating Captions for Scientific Figurescited by 77
  2. 2016Re-evaluating Automatic Metrics for Image Captioningcited by 45
  3. 2021Accessible Visualization via Natural Language Descriptions: A Four-Level Model of Semantic Contentcited by 39
  4. 2019Answering Questions about Data Visualizations using Efficient Bimodal Fusioncited by 26
  5. 2021Towards Understanding How Readers Integrate Charts and Captions: A Case Study with Line Chartscited by 12
  6. 2021Truth-Conditional Captioning of Time Series Datacited by 11

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 4; 2024: 4; 2025: 8; 2026: 1.

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  1. 2023UniChart: A Universal Vision-language Pretrained Model for Chart Comprehension and Reasoningcited by 160
  2. 2023VisText: A Benchmark for Semantically Rich Chart Captioningcited by 112
  3. 2024From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Modelscited by 53
  4. 2023ChartReader: A Unified Framework for Chart Derendering and Comprehension without Heuristic Rulescited by 24
  5. 2023Summaries as Captions: Generating Figure Captions for Scientific Documents with Automated Text Summarizationcited by 14
  6. 2025Time Series Language Model for Descriptive Caption Generationcited by 11
  7. 2025PatentLMM: Large Multimodal Model for Generating Descriptions for Patent Figurescited by 7
  8. 2025ChartMind: A Comprehensive Benchmark for Complex Real-world Multimodal Chart Question Answeringcited by 7
  9. 2025DualSG: A Dual-Stream Explicit Semantic-Guided Multivariate Time Series Forecasting Frameworkcited by 7
  10. 2024Domain-Independent Automatic Generation of Descriptive Texts for Time-Series Datacited by 6
  11. 2024Natural Language Generation for Visualizations: State of the Art, Challenges and Future Directionscited by 6
  12. 2024SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratingscited by 5
5 more
  1. 2025Generalization of CNNs on Relational Reasoning with Bar Chartscited by 3
  2. 2025TADACap: Time-series Adaptive Domain-Aware Captioningcited by 3
  3. 2026ChartFI: Benchmarking Faithfulness and Insightfulness of Chart Descriptions from Multimodal Large Language Modelscited by 2
  4. 2025DiffNator: Generating Structured Explanations of Time-Series Differencescited by 0
  5. 2025Benchmark It Yourself (BIY): Preparing a Dataset and Benchmarking AI Models for Scatterplot-Related Taskscited by 0