  Webpage information extraction (WIE) is an important step to create knowledge
bases. For this, classical WIE methods leverage the Document Object Model (DOM)
tree of a website. However, use of the DOM tree poses significant challenges as
context and appearance are encoded in an abstract manner. To address this
challenge we propose to reformulate WIE as a context-aware Webpage Object
Detection task. Specifically, we develop a Context-aware Visual Attention-based
(CoVA) detection pipeline which combines appearance features with syntactical
structure from the DOM tree. To study the approach we collect a new large-scale
dataset of e-commerce websites for which we manually annotate every web element
with four labels: product price, product title, product image and background.
On this dataset we show that the proposed CoVA approach is a new challenging
baseline which improves upon prior state-of-the-art methods.
