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.
@article{arxiv.2110.12320,
title = {CoVA: Context-aware Visual Attention for Webpage Information Extraction},
author = {Anurendra Kumar and Keval Morabia and Jingjin Wang and Kevin Chen-Chuan Chang and Alexander Schwing},
journal= {arXiv preprint arXiv:2110.12320},
year = {2022}
}