SciCap: Generating Captions for Scientific Figures
Ting-Yao Hsu, Clyde Lee Giles, Ting-Hao Huang · 2021
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.