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Web Image Context Extraction (WICE) consists in obtaining the textualinformation describing an image using the content of the surrounding webpage. Acommon preprocessing step before performing WICE is to render the content ofthe webpage. When done at a large scale (e.g., for search engine indexation),it may become very computationally costly (up to several seconds per page). Toavoid this cost, we introduce a novel WICE approach that combines Graph NeuralNetworks (GNNs) and Natural Language Processing models. Our method relies on agraph model containing both node types and text as features. The model is fedthrough several blocks of GNNs to extract the textual context. Since no labeledWICE dataset with ground truth exists, we train and evaluate the GNNs on aproxy task that consists in finding the semantically closest text to the imagecaption. We then interpret importance weights to find the most relevant textnodes and define them as the image context. Thanks to GNNs, our model is ableto encode both structural and semantic information from the webpage. We showthat our approach gives promising results to help address the large-scale WICEproblem using only HTML data.

Web Image Context Extraction with Graph Neural Networks and Sentence Embeddings on the DOM tree