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 dataset1 of e-commerce websites for which we manually annotate every web element with four labels: product price, product title, product image and others. On this dataset we show that the proposed CoVA approach is a new challenging baseline which improves upon prior state-of-the-art methods.
CITATION STYLE
Kumar, A., Morabia, K., Wang, J., Chang, K. C. C., & Schwing, A. (2022). CoVA: Context-aware Visual Attention for Webpage Information Extraction. In ECNLP 2022 - 5th Workshop on e-Commerce and NLP, Proceedings of the Workshop (pp. 80–90). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2022.ecnlp-1.11
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