Python: Detecting the actual text paragraphs in a string
A general solution to this problem is a non-trivial problem to solve.
To put this in context, a large part of Google's success with search has come from their ability to automatically discern some semantic meaning from arbitrary Web pages, namely figuring out where the "content" is.
One idea that springs to mind is if you can crawl many pages from the same site then you will be able to identify patterns. Menu markup will be largely the same between all pages. If you zero this out somehow (and it will need to fairly "fuzzy") what's left is the content.
The next step would be to identify the text and what constitutes a boundary. Ideally that would be some HTML paragraphs but you won't get that lucky most of the time.
A better approach might be to find the RSS feeds for the site and get the content that way because that will be stripped down as is. Ignore any AdSense (or similar) content and you should be able to get the text.
Oh and absolutely throw out your regex code for this. This requires an HTML parser absolutely without question.
The big mission: I am trying to get a few lines of summary of a webpage. i.e. I want to have a function that takes a URL and returns the most informative paragraph from that page. (Which would usually be the first paragraph of actual content text, in contrast to "junk text", like the navigation bar.)
So I managed to reduce an HTML page to a bunch of text by cutting out the tags, throwing out the <HEAD> and all the scripts. But some of the text is still "junk text". I want to know where the actual paragraphs of text begin. (Ideally it should be human-language-agnostic, but if you have a solution only for English, that might help too.)
How can I figure out which of the text is "junk text" and which is actual content?
UPDATE: I see some people have pointed me to use an HTML parsing library. I am using Beautiful Soup. My problem isn't parsing HTML; I already got rid of all the HTML tags, I just have a bunch of text and I want to separate the context text from the junk text.