Do a simple experiment: get some texts, split words between spaces (e.g line.split(" ")) and use a dict to count the frequency of the words. Sort the words by frequency, look at them, and you will eventually reach the same conclusion as in figure 1 of the paper by Luhn when working for IBM in 1958 (http://courses.ischool.berkeley.edu/i256/f06/papers/luhn58.p...)
There are lots of corpora out there in the wild, but if you need to roll your own from wikipedia texts you can use this tool I did: https://github.com/joaoventura/WikiCorpusExtractor
From this experiment, and depending if you like statistics or not, you can play a bit with the numbers. For instance, you can use Tf-Idf (https://en.wikipedia.org/wiki/Tf%E2%80%93idf) to extract potential keywords from documents. Check the formula, it only uses the frequency of occurrence of words in documents.
Only use tools such as Deep neural networks if you decide later that they are essential for what you need. I did an entire PhD on this area just with Python and playing with frequencies, no frameworks at all (an eg. of my work can be found at http://www.sciencedirect.com/science/article/pii/S1877050912...).
Good luck!