GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
Graphical Abstracts (GAs) visually summarize the key findings of academic papers, playing a crucial role in facilitating the understanding of research content. Recently, advancements in vision-language models and image generation models have enabled the automatic generation of scientific figures based on paper content. However, most conventional methods output the generated results as raster graphics, making post-editing (e.g., text modification and layout changes) highly difficult. This poses a significant challenge, as they are unsuitable for the iterative figure revision process inherent in paper writing and peer review. To tackle these challenges, we define the novel task of generatin...
| Authors | Takuro Kawada, Shunsuke Kitada, Hitoshi Iyatomi |
|---|---|
| Primary category | cs.GR |
| Published | 5 August 2026 |
| Last updated at source | 5 August 2026 |
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