In this paper we analyze news text collections (clusters) via extracting their paraphrase headlines into a paraphrase graph and working with this graph. Our aim is to test whether news headline is an appropriate form of news text compression. Different types of news collections: dynamic, static and combined (both dynamic and static) clusters are analyzed and it is shown that their respective paraphrase graphs reflect the characteristics of the texts. We also automatically extract the most informationally important linked fragments of news texts, and these fragments characterize news texts as either informative, conveying some information, or publicistic ones, trying to affect the readers emotionally. It is shown that news headlines of the informative type do represent their respective compressed news reports.