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            <title>Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)</title>
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    <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 datase 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.</abstract>
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%0 Conference Proceedings
%T CoVA: Context-aware Visual Attention for Webpage Information Extraction
%A Kumar, Anurendra
%A Morabia, Keval
%A Wang, William
%A Chang, Kevin
%A Schwing, Alex
%Y Malmasi, Shervin
%Y Rokhlenko, Oleg
%Y Ueffing, Nicola
%Y Guy, Ido
%Y Agichtein, Eugene
%Y Kallumadi, Surya
%S Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F kumar-etal-2022-cova
%X 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 datase 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.
%R 10.18653/v1/2022.ecnlp-1.11
%U https://aclanthology.org/2022.ecnlp-1.11
%U https://doi.org/10.18653/v1/2022.ecnlp-1.11
%P 80-90

Markdown (Informal)

[CoVA: Context-aware Visual Attention for Webpage Information Extraction](https://aclanthology.org/2022.ecnlp-1.11) (Kumar et al., ECNLP 2022)

• CoVA: Context-aware Visual Attention for Webpage Information Extraction (Kumar et al., ECNLP 2022)

ACL

• Anurendra Kumar, Keval Morabia, William Wang, Kevin Chang, and Alex Schwing. 2022. CoVA: Context-aware Visual Attention for Webpage Information Extraction. In Proceedings of the Fifth Workshop on e-Commerce and NLP (ECNLP 5), pages 80–90, Dublin, Ireland. Association for Computational Linguistics.