Abstract
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, a large-scale 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.- Anthology ID:
- 2021.findings-emnlp.277
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3258–3264
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.277
- DOI:
- 10.18653/v1/2021.findings-emnlp.277
- Cite (ACL):
- Ting-Yao Hsu, C Lee Giles, and Ting-Hao Huang. 2021. SciCap: Generating Captions for Scientific Figures. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 3258–3264, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- SciCap: Generating Captions for Scientific Figures (Hsu et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2021.findings-emnlp.277.pdf
- Code
- tingyaohsu/scicap
- Data
- SCICAP, FigureQA