Hierarchical Multimodal Pre-training for Visually Rich Webpage
  Understanding
Hongshen Xu Lu Chen Zihan Zhao Da Ma Ruisheng Cao Zichen Zhu Kai Yu
The growing prevalence of visually rich documents, such as webpages and
scanned/digital-born documents (images, PDFs, etc.), has led to increased
interest in automatic document understanding and information extraction across
academia and industry. Although various document modalities, including image,
text, layout, and structure, facilitate human information retrieval, the
interconnected nature of these modalities presents challenges for neural
networks. In this paper, we introduce WebLM, a multimodal pre-training network
designed to address the limitations of solely modeling text and structure
modalities of HTML in webpages. Instead of processing document images as
unified natural images, WebLM integrates the hierarchical structure of document
images to enhance the understanding of markup-language-based documents.
Additionally, we propose several pre-training tasks to model the interaction
among text, structure, and image modalities effectively. Empirical results
demonstrate that the pre-trained WebLM significantly surpasses previous
state-of-the-art pre-trained models across several webpage understanding tasks.
The pre-trained models and code are available at
https://github.com/X-LANCE/weblm.