Although it may not be immediately obvious, just being able to perform reasonably good sentence detection as part of an NLP approach to mining unstructured data can enable some pretty powerful text-mining capabilities, such as crude but very reasonable attempts at document summarization. There are numerous possibilities and approaches, but one of the simplest to get started with dates all the way back to the April 1958 issue of IBM Journal. In the seminal article entitled “The Automatic Creation of Literature Abstracts,” H.P. Luhn describes a technique that essentially boils down to filtering out sentences containing frequently occurring words that appear near one another.