DocSynthv2: A Practical Autoregressive Modeling for Document Generation
<|reference_start|>DocSynthv2: A Practical Autoregressive Modeling for Document Generation: 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.<|reference_end|>
@article{biswas2024docsynthv2:,
    title={DocSynthv2: A Practical Autoregressive Modeling for Document Generation},
    author={Sanket Biswas, Rajiv Jain, Vlad I. Morariu, Jiuxiang Gu, Puneet
  Mathur, Curtis Wigington, Tong Sun, Josep Llad'os},
    journal={arXiv preprint arXiv:2406.08354},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.08354},
    primaryClass={cs.CV cs.AI cs.LG}
}