The concept is rather simple: use information about the density of text vs. HTML code to work out if a line of text is worth outputting. (This isn’t a novel idea, but it works!) The basic process works as follows: 1. Parse the HTML code and keep track of the number of bytes processed. 2. Store the text output on a per-line, or per-paragraph basis. 3. Associate with each text line the number of bytes of HTML required to describe it. 4. Compute the text density of each line by calculating the ratio of text to bytes. 5. Then decide if the line is part of the content by using a neural network.
You can get pretty good results just by checking if the line’s density is above a fixed threshold (or the average), but the system makes fewer mistakes if you use machine learning 
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