I have started to learn text mining and natural language processing using R and Python. Recently, I was trying to perform some basics tasks such as: (1) the most frequent terms used within a set of documents (email documents) and (2) clustering. The "problem" comes with some repetitive paragraphs, such as disclaimers, signatures on emails, etc; because they are adding some noise to my results.... Is there a way to identify boilerplate or repetitive paragraphs within the set of documents? In order to remove them during the preprocessing tasks.
Measuring the similarity of documents is a huge topic, and an active area of research. There are many ways of identifying boilerplate, none of them perfect.  
But check out the wydyr package functions.  Break documents into paragraph-size sections (or smaller.)  Use pairwise_count and pairwise_cor to get similarity measures between, e.g. the opening and closing sections of documents.
Also, get a copy of Text Mining with R by Silge and Robinson; pay attention to Chapter 4.
Identify duplicated paragraphs (boilerplate) within several email documents