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==Literature==
All articles in each section are listed in order of relevance to the project.
=== HTML Tree Structure Analysis ===
* [http://cs229.stanford.edu/proj2013/ZhouMashuq-WebContentExtractionThroughMachineLearning.pdf Web Content Extraction Through Machine Learning (Zhou, Mashuq)]
:This approach to web content extraction focus focuses exclusively on less structured web pages, and classifying text blocks within those pages. In Section 3: Data Collection, an algorithm written in JavaScript is used to inspect DOM elements and organize them by parent element. * [http://cs229.stanford.edu/proj2013/YaoZuo-AMachineLearningApproachToWebpageContentExtraction.pdf A Machine Learning Approach to Webpage Content Extraction (Yao, Zuo)]: This article describes methods to simplify the content of a noisy HTML page, specifically through using machine learning to predict whether a block is content or non-content. This allows the classifier to remove boilerplate information.
* [https://arxiv.org/abs/1207.0246 Web Data Extraction, Applications and Techniques: A Survey (Ferrara et al.)]
* [https://arxiv.org/abs/1709.05584 Representation Learning on Graphs: Methods and Application (Hamilton, Ying, Leskovec)]
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* [https://arxiv.org/abs/1607.00653 node2vec: Scalable Feature Learning for Networks (Grover, Leskovec)]
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=== General ===
* [http://cs229.stanford.edu/proj2013/YaoZuo-AMachineLearningApproachToWebpageContentExtraction.pdf A Machine Learning Approach to Webpage Content Extraction (Yao, Zuo)]
: This article describes methods to simplify the content of a noisy HTML page, specifically through using machine learning to predict whether a block is content or non-content. This allows the classifier to remove boilerplate information.
