The traditional solution to this problem is to use the vector representation for the news stories and then cluster the vectors. The vectors are arrays where each entry represents a word or word class. The value associated to each word will be the tf-idf weight. This value goes up the more frequent the word in the document and down the more frequent the word is in the whole collection of documents.
You may think of the titles as the documents, but sticking to just the title for news stories may be a bit risky for clustering similar stories. The problem is that by using word counts you are discarding all information on the order of the words. Longer texts compensate for that loss information by distinguishing documents by the vocabulary used (articles mentioning finance, money, ... are closer to each other than those mentioning ergodic, Poincare).
If you want to stick to titles, one idea is to think of word pairs as the words you use in the vector representation. So for the title The eagle has landed, you would think of the eagle, eagle has, has landed. as the “words.”
To discover when a cluster has become much bigger or different from the others you will need to develop a decision procedure.