GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers

• URL: http://arxiv.org/abs/2608.05478v1
• Date: Wed, 05 Aug 2026 23:53:02 GMT
• Title: GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
• Abstract summary: We propose GenGA, a new generation framework that directly produces figures in vector format.<n>By generating figures as a collection of vector elements with a hierarchical structure, GenGA produces outputs that can be seamlessly imported into existing drawing tools.<n> Experimental results show that GenGA achieves superior editing simplicity compared to other methods, and even surpasses human-authored GAs in conciseness and semantic alignment.
• Score: 2.593510481959727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 generating editable GAs from paper content and propose GenGA, a new GA generation framework that directly produces figures in vector format. By generating figures as a collection of vector elements with a hierarchical structure, GenGA produces outputs that can be seamlessly imported into existing drawing tools for intuitive, element-level editing. Furthermore, we introduce the Structural Independence Coefficient (SIC), a metric that quantifies the editing simplicity of a figure based on the degree to which local modifications propagate to other elements. Experimental results show that GenGA achieves superior editing simplicity compared to conventional methods, and even surpasses human-authored GAs in conciseness and semantic alignment. We also validate SIC as an effective metric correlated with manual editing costs. This study fundamentally redefines GA generation as an editable vector graphic generation problem grounded in the practical workflows of researchers, significantly promoting effective scientific communication.

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