  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.
