How to detect the "fingerprint" of an article or a long string?


It just occurs to me that since some fingerprint reader can quickly find out if two fingerprints are similar or not, can we treat every article as a fingerprint and detect if any two of many articles are similar or not? In other words, how to find the uniqueness or pattern of a few words of a long string which can represent as much of the original string as possible?

Any suggestions or ideas are appreciated!
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This article covers pretty much what you want to know

You may want to read the introduction at the top of the page or just dive into the theory.

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You could build a dictionary table (hashed) of interesting words or phrases in one document, (ignore common usage words) then do the same for the second document and use the number of collisions as a way to see where the same word/phrases are used. By weighting some words/hashes differently you can create a way to measure sameness. To get fancier you can use some fuzzy matching/logic
Many of the required ideas would also be used for plagiarism detection:
Here is an EE article to review:
A Guide to Approximate String Matching
You can generate a unique "fingerprint" for each document by generating a hash code for it with an algorithm like MD5.  This will not help with near-duplicate detection, only exact dupes but it one of the simplest possible solutions.  You may also want to research Latent Semantic Indexing, which does exactly what you want but tends to require some pretty sophisticated software and, depending on the size of your document set, fairly beefy hardware as well.
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