Here is an example of how to render this feature for an article from the France Musique website. It is based on work initiated by the Arc90 lab. The code behind this feature is written in JavaScript and is called readability by Mozilla. Have you ever noticed the button in the URL bar of Firefox? It’s the Reader View: it allows you to remove the boilerplate from HTML pages to keep only the main text content. trafilatura also uses jusText to perform some processing. text length, position/depth of the element in the HTML, or word count. Trafilatura, also created in Python, offers heuristics on both the HTML element type and its content, e.g. You can read more about the algorithm in the documentation. These heuristics are mostly based on the number of words, the text/code ratio, the presence or absence of links, etc. The method allows text blocks from HTML to be categorized as “good”, “bad”, “too short” according to different heuristics. JusText is a proposed implementation in Python from a PhD thesis: “Removing Boilerplate and Duplicate Content from Web Corpora”. We’re mostly interested in Open Source projects in Python or JavaScript.

There are several methods for doing this. Overall, we would like to get rid of everything that “hangs around” the main text: menus and other sidebars, contact elements, footer links, etc. That’s why it’s essential that the extraction is as qualitative as possible. It is therefore only this text that we want to pass to machine learning models for processing. We want to get rid of it and keep only a part: the text that carries relevant information. These HTML elements that appear on almost all your pages are called boilerplate. When you read an article about John Coltrane’s last posthumous album, for example, you ignore the menus, the footer, … and obviously, you aren’t viewing the whole HTML content. However, we want to extract text that makes sense, that is as informative as possible. If you want to contourn this issue by taking advantage of our JS crawler in your crawls, I suggest you read “How to crawl a site in JavaScript?”. If you ignore for a few minutes the fact that more and more sites use JavaScript rendering engines like Vue.js or React, parsing HTML is not very complicated. With a few lines of Python, for example, a couple of regular expressions (regexp), a parsing library like BeautifulSoup4, and it’s done. ProblemĮxtracting text content from a web page might seem simple. Through this article, I propose to explore the problem and to discuss some tools and recommendations to achieve this task. When we talk about web pages, this includes the HTML, JavaScript, menus, media, header, footer, … Automatically and correctly extracting content is not easy. The first step in this adventure is to extract the text content of the web pages that these machine learning models will use. Optimising content according to your Google Search Console data.Adding new tags to your articles or correcting existing ones.Using machine learning models for natural language processing (NLP), it is possible to carry out tasks with real added value for SEO. In Oncrawl’s R&D department, we are increasingly looking to add value to the semantic content of your web pages.

Extract text from html online