  Identifying high-quality webpages is fundamental for real-world search
engines, which can fulfil users' information need with the less cognitive
burden. Early studies of \emph{webpage quality assessment} usually design
hand-crafted features that may only work on particular categories of webpages
(e.g., shopping websites, medical websites). They can hardly be applied to
real-world search engines that serve trillions of webpages with various types
and purposes. In this paper, we propose a novel layout-aware webpage quality
assessment model currently deployed in our search engine. Intuitively, layout
is a universal and critical dimension for the quality assessment of different
categories of webpages. Based on this, we directly employ the meta-data that
describes a webpage, i.e., Document Object Model (DOM) tree, as the input of
our model. The DOM tree data unifies the representation of webpages with
different categories and purposes and indicates the layout of webpages. To
assess webpage quality from complex DOM tree data, we propose a graph neural
network (GNN) based method that extracts rich layout-aware information that
implies webpage quality in an end-to-end manner. Moreover, we improve the GNN
method with an attentive readout function, external web categories and a
category-aware sampling method. We conduct rigorous offline and online
experiments to show that our proposed solution is effective in real search
engines, improving the overall usability and user experience.
