Could one algorithm fetch keywords from texts of different natural languages?
There are a few ways to deal with this issue.  Python has a package called NLTK which contains stop word lists for several languages (including English and Norwegian).  You can simply use this package, it's usage is as follows:

&gt;&gt;&gt; from nltk.corpus import stopwords
&gt;&gt;&gt; stop = stopwords.words('english')
&gt;&gt;&gt; sentence = "this is a foo bar sentence"
&gt;&gt;&gt; print [i for i in sentence.split() if i not in stop]
  ['foo', 'bar', 'sentence']


Alternatively, a method for automatically suppressing stop words is called tf-idf;  tf-idf is commonly used in search engines so that the most important words are promoted to the forefront.  In your case, I would suspect you'd want to have IDF scores for both English and Norwegian and apply only the appropriate one on a language to language basis.

http://en.wikipedia.org/wiki/Tf%E2%80%93idf