While the generation of document layouts has been extensively explored, comprehensive document generation encompassing both layout and content presents a more complex challenge. This paper delves into this advanced domain, proposing a novel approach called DocSynthv2 through the development of a simple yet effective autoregressive structured model. Our model, distinct in its integration of both layout and textual cues, marks a step beyond existing layout-generation approaches. By focusing on the relationship between the structural elements and the textual content within documents, we aim to generate cohesive and contextually relevant documents without any reliance on visual components. Through experimental studies on our curated benchmark for the new task, we demonstrate the ability of our model combining layout and textual information in enhancing the generation quality and relevance of documents, opening new pathways for research in document creation and automated design. Our findings emphasize the effectiveness of autoregressive models in handling complex document generation tasks.

翻译：尽管文档布局生成已得到广泛探索，但涵盖布局与内容的完整文档生成提出了更为复杂的挑战。本文深入这一前沿领域，通过开发一种简洁而有效的自回归结构化模型，提出了一种名为DocSynthv2的新方法。我们的模型独特之处在于融合了布局与文本线索，标志着对现有布局生成方法的超越。通过聚焦文档内部结构元素与文本内容之间的关系，我们的目标是在完全不依赖视觉组件的情况下生成连贯且上下文相关的文档。在我们为新任务构建的基准测试上进行实验研究后，我们证明了模型结合布局与文本信息能够提升文档的生成质量与相关性，为文档创建与自动化设计研究开辟了新路径。我们的研究结果凸显了自回归模型在处理复杂文档生成任务中的有效性。