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

• URL: http://arxiv.org/abs/2403.17784v1
• Date: Tue, 26 Mar 2024 15:16:14 GMT
• Title: SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
• Authors: Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang, Ryan Rossi, Sungchul Kim, Tong Yu, C. Lee Giles, Ting-Hao K. Huang,
• Abstract summary: This paper introduces SciCapenter, an interactive system that puts together cutting-edge AI technologies for scientific figure captions. SciCapenter generates a variety of captions for each figure in a scholarly article, providing scores and a comprehensive checklist to assess caption quality. A user study with Ph.D. students indicates that SciCapenter significantly lowers the cognitive load of caption writing.
• Score: 28.973082312034343
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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