  Good figure captions help paper readers understand complex scientific
figures. Unfortunately, even published papers often have poorly written
captions. Automatic caption generation could aid paper writers by providing
good starting captions that can be refined for better quality. Prior work often
treated figure caption generation as a vision-to-language task. In this paper,
we show that it can be more effectively tackled as a text summarization task in
scientific documents. We fine-tuned PEGASUS, a pre-trained abstractive
summarization model, to specifically summarize figure-referencing paragraphs
(e.g., "Figure 3 shows...") into figure captions. Experiments on large-scale
arXiv figures show that our method outperforms prior vision methods in both
automatic and human evaluations. We further conducted an in-depth investigation
focused on two key challenges: (i) the common presence of low-quality
author-written captions and (ii) the lack of clear standards for good captions.
Our code and data are available at:
https://github.com/Crowd-AI-Lab/Generating-Figure-Captions-as-a-Text-Summarization-Task.
