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.

通过开发一种简单且有效的自回归结构模型，本文在探索综合文档生成，即包括布局和内容的文档生成的更复杂挑战方面提出了名为 DocSynthv2 的新方法，通过关注文档内结构元素与文本内容之间的关系，我们旨在生成连贯且与上下文相关的文档，不依赖于视觉组件，实验证明了我们的模型在提高文档生成质量和相关性方面结合布局和文本信息的能力，为文档创建和自动设计的研究开辟了新的路径。

DocSynthv2：面向文档生成的实用自回归建模

DocSynthv2: A Practical Autoregressive Modeling for Document Generation

Authors seeking to communicate with broader audiences often compose their
ideas about the same underlying knowledge in different documents and formats --
for example, as slide decks, newsletters, reports, brochures, etc. Prior work
in document generation has generally considered the creation of each separate
format to be different a task, developing independent methods for generation
and evaluation. This approach is suboptimal for the advancement of AI-supported
content authoring from both research and application perspectives because it
leads to fragmented learning processes, redundancy in models and methods, and
disjointed evaluation. Thus, in our work, we consider each of these documents
to be templatic views of the same underlying knowledge, and we aim to unify the
generation and evaluation of these templatic views of documents. We begin by
introducing an LLM-powered method to extract the most important information
from an input document and represent this information in a structured format.
We show that this unified representation can be used to generate multiple
templatic views with no supervision and with very little guidance, improving
over strong baselines. We additionally introduce a unified evaluation method
that is template agnostic, and can be adapted to building document generators
for heterogeneous downstream applications. Finally, we conduct a human
evaluation, which shows that humans prefer 82% of the downstream documents
generated with our method. Furthermore, the newly proposed evaluation metric
correlates more highly with human judgement than prior metrics, while providing
a unified evaluation method.

使用一个 LLM-powered 方法从输入文档中提取最重要的信息并以结构化格式表示，然后生成多个模板视图，无需监督或很少的指导，提高生成文档的质量，并引入了统一的评估方法，该方法不受模板的影响，可适应多样的下游应用。