As the number of scientific papers continues to grow, there is a demand for approaches that can effectively convey research findings, with posters serving as a key medium for presenting paper contents. Poster layouts determine how effectively research is communicated and understood, highlighting their growing importance. In particular, a gap remains in understanding how papers correspond to the layouts that present them, which calls for datasets with paired annotations at scale. To bridge this gap, we introduce SciPostGen, a large-scale dataset for understanding and generating poster layouts from scientific papers. Our analyses based on SciPostGen show that paper structures are associated with the number of layout elements in posters. Based on this insight, we explore a framework, Retrieval-Augmented Poster Layout Generation, which retrieves layouts consistent with a given paper and uses them as guidance for layout generation. We conducted experiments under two conditions: with and without layout constraints typically specified by poster creators. The results show that the retriever estimates layouts aligned with paper structures, and our framework generates layouts that also satisfy given constraints. The dataset and code are publicly available at https://omron-sinicx.github.io/paper2layout/.

翻译：摘要：随着科学论文数量的持续增长，需要能够有效传达研究成果的方法，而海报作为展示论文内容的关键载体。海报布局决定了研究成果的传达和理解效果，凸显其日益增长的重要性。特别地，论文与其呈现布局之间的对应关系仍存在认知空白，这亟需大规模带有配对标注的数据集。为此，我们提出SciPostGen——一个用于从科学论文理解并生成海报布局的大规模数据集。基于SciPostGen的分析表明，论文结构与海报中的布局元素数量存在关联。基于这一发现，我们探索了检索增强型海报布局生成框架，该框架能检索与给定论文一致的布局并将其作为布局生成的指引。我们在两种条件下进行实验：有无海报创作者通常指定的布局约束。结果表明，检索器能估计与论文结构一致的布局，且我们的框架能生成同时满足给定约束的布局。数据集和代码已在 https://omron-sinicx.github.io/paper2layout/ 公开。