The method of extracting relevant content from a webpage while removing irrelevant (noisy) content such as advertisements, table of contents, header and footer, and so on is known as webpage content extraction. It's also known as boilerplate removal or boilerplate removal.
Extracting clean textual material from the Web is the first and most critical step in resolving most down-stream natural language processing tasks. Previous research on web content extraction has mostly focused on web pages with a single key textual content block, such as news articles and blog posts. They use techniques that heavily rely on the website's HTML structure to extract the key information.
By extracting only the most appropriate and applicable material from web pages, content extraction algorithms promise a wide range of applications. For starters, the derived content is more readable for humans, which is a critical criterion for third-party data users like an app that subscribes to a website's content. Second, by eliminating distracting textual material from the web page, such as ads, headers, and footers, it improves the efficiency of downstream natural language processing tasks like text classification and summarisation.
Since the inception of the World Wide Web, the content extraction issue has been a research topic. Its aim is to differentiate a webpage's key content, such as a news story's text, from the distracting content, such as advertising and navigation links. The majority of content extraction methods work on a block level, which means that the webpage is segmented into blocks, and then each of these blocks is defined to be part of the webpage's main content or noisy content. We try to apply content extraction at a deeper level in this thesis, namely to HTML elements.
The webpages (also referred to web documents) that constitute the World Wide Web are sources of very diverse categories of information. These include, among other things, news, reference materials, forum discussions, and commercial product descriptions. Each type of information can be represented in a variety of media formats, including text, graphics, and video. This massive amount of data is used by ordinary web users all over the world, as well as automated crawlers that scour the Web for various purposes including web mining and indexing.