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.
@article{arxiv.2110.11624,
title = {SciCap: Generating Captions for Scientific Figures},
author = {Ting-Yao Hsu and C. Lee Giles and Ting-Hao 'Kenneth' Huang},
journal= {arXiv preprint arXiv:2110.11624},
year = {2021}
}
Comments
To Appear in EMNLP 2021 Findings. The dataset is available at: https://github.com/tingyaohsu/SciCap