The DOI matches its Crossref record; 33 of the 33 references it cites were found in a registry or an index.
SciCap: Generating Captions for Scientific Figures
Open accessAbstract
Researchers use figures to communicate rich, complex information in scientific papers.The captions of these figures are critical to conveying effective messages.However, low-quality figure captions commonly occur in scientific articles and may decrease understanding.In this paper, we propose an end-to-end neural framework to automatically generate informative, high-quality captions for scientific figures.To this end, we introduce SCICAP, 1 a largescale figure-caption dataset based on computer science arXiv papers published between 2010 and 2020.After pre-processing -including figure-type classification, sub-figure identification, text normalization, and caption text selection -SCICAP contained more than two million figures extracted from over 290,000 papers.We then established baseline models that caption graph plots, the dominant (19.2%) figure type.The experimental results showed both opportunities and steep challenges of generating captions for scientific figures.
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“Research has explored methods to automate alt text creation, but has yielded mixed results [30, 52, 81, 85].”
From How University Disability Services Professionals Write Image Descriptions for HCI Figures Using Generative AI (2026) · cites this paper as [30]
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Metadata
- Authors
- Hsu, T.-Y., Giles, C. L., & Huang, T.-H.
- Affiliations
- Pennsylvania State University (US)
- Venue
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Published
- 2021
- Pages
- 3258-3264
- OpenAlex
- W3210140424
- Cited by
- 59
- References
- 33
- Citations by year
- 2026: 11 · 2025: 13 · 2024: 7 · 2023: 18 · 2022: 9 · 2021: 1
- FWCI
- 2.86
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Strong
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Handwritten Text Recognition Techniques
- Keywords
- automatic caption generation, scientific figures, figure captions, text normalization
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