Crafting effective captions for figures is important. Readers heavily depend
on these captions to grasp the figure's message. However, despite a
well-developed set of AI technologies for figures and captions, these have
rarely been tested for usefulness in aiding caption writing. This paper
introduces SciCapenter, an interactive system that puts together cutting-edge
AI technologies for scientific figure captions to aid caption composition.
SciCapenter generates a variety of captions for each figure in a scholarly
article, providing scores and a comprehensive checklist to assess caption
quality across multiple critical aspects, such as helpfulness, OCR mention, key
takeaways, and visual properties reference. Users can directly edit captions in
SciCapenter, resubmit for revised evaluations, and iteratively refine them. A
user study with Ph.D. students indicates that SciCapenter significantly lowers
the cognitive load of caption writing. Participants' feedback further offers
valuable design insights for future systems aiming to enhance caption writing.

SciCapenter 是一个交互式系统，使用尖端的人工智能技术为科学图解提供多样的说明，通过评估说明质量的得分和综合检查表，显著降低了科学说明撰写的认知负荷。

SciCapenter: 支持科学图表的标题组合与机器生成的标题和评级

SciCapenter: Supporting Caption Composition for Scientific Figures with  Machine-Generated Captions and Ratings

In scholarly documents, figures provide a straightforward way of
communicating scientific findings to readers. Automating figure caption
generation helps move model understandings of scientific documents beyond text
and will help authors write informative captions that facilitate communicating
scientific findings. Unlike previous studies, we reframe scientific figure
captioning as a knowledge-augmented image captioning task that models need to
utilize knowledge embedded across modalities for caption generation. To this
end, we extended the large-scale SciCap
dataset~\cite{hsu-etal-2021-scicap-generating} to SciCap+ which includes
mention-paragraphs (paragraphs mentioning figures) and OCR tokens. Then, we
conduct experiments with the M4C-Captioner (a multimodal transformer-based
model with a pointer network) as a baseline for our study. Our results indicate
that mention-paragraphs serves as additional context knowledge, which
significantly boosts the automatic standard image caption evaluation scores
compared to the figure-only baselines. Human evaluations further reveal the
challenges of generating figure captions that are informative to readers. The
code and SciCap+ dataset will be publicly available at
this https URL

通过扩充 SciCap 数据集，创新性地将科技论文中的图例标注生成作为知识增强图像标注任务，提高自动标注的效率与准确性，并探讨了从不同模视觉图像和文本知识融合的角度进行标注生成的实验研究。