  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.
