CoVA: Context-aware Visual Attention for Webpage Information Extraction

• URL: http://arxiv.org/abs/2110.12320v1
• Date: Sun, 24 Oct 2021 00:21:46 GMT
• Title: CoVA: Context-aware Visual Attention for Webpage Information Extraction
• Authors: Anurendra Kumar, Keval Morabia, Jingjin Wang, Kevin Chen-Chuan Chang, Alexander Schwing
• Abstract summary: We propose to reformulate WIE as a context-aware Webpage Object Detection task. We develop a Context-aware Visual Attention-based (CoVA) detection pipeline which combines appearance features with syntactical structure from the DOM tree. We show that the proposed CoVA approach is a new challenging baseline which improves upon prior state-of-the-art methods.
• Score: 65.11609398029783
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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