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

CoVA: Context-aware Visual Attention for Webpage Information Extraction