GenGA：面向学术论文的可编辑且基于数据的图形摘要生成
GenGA: Editable and Data-Grounded Graphical Abstract Generation for Academic Papers
- Hosei University（法政大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
GenGA是一种直接生成矢量格式图形摘要的框架，可实现便捷的元素级编辑，其编辑简易性优于传统方法，且在简洁性和语义对齐上超过人工生成的图形摘要，还提出了量化编辑简易性的SIC指标。
AI中文摘要：
图形摘要（Graphical Abstracts，GAs）以可视化形式总结学术论文的关键发现，在促进研究内容理解方面发挥着关键作用。近年来，视觉-语言模型和图像生成模型的进展已能够基于论文内容自动生成科学图表。然而，大多数传统方法将生成结果输出为光栅图形，使得后期编辑（如文本修改和布局调整）极为困难。这构成了重大挑战，因为这些方法不适用于论文撰写和同行评审所固有的迭代图表修订流程。为应对这些挑战，我们定义了从论文内容生成可编辑图形摘要的新任务，并提出GenGA，这是一种直接生成矢量格式图表的新型图形摘要生成框架。通过将图表生成为具有分层结构的矢量元素集合，GenGA生成的输出可无缝导入现有绘图工具，实现直观的元素级编辑。此外，我们引入结构独立性系数（Structural Independence Coefficient，SIC），这是一种基于局部修改向其他元素的传播程度来量化图表编辑简易性的指标。实验结果表明，与传统方法相比，GenGA实现了更优的编辑简易性，且在简洁性和语义对齐方面甚至超过了人工生成的图形摘要。我们还验证了SIC是与手动编辑成本相关的有效指标。本研究从根本上将图形摘要生成重新定义为基于研究者实际工作流程的可编辑矢量图形生成问题，显著促进了有效的科学交流。
英文摘要：
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.
