<p>Text summarization is an important method that compresses massive amounts of information into clear, succinct summaries that make it easier to grasp and extract knowledge. Text summarization tasks can be broadly divided into two types: extractive and abstractive. In this paper, the task of Extractive Summarization for Dogri language is taken up. The goal of Extractive Summarization is to extract key phrases from text into a meaningful form. The Dogri Extractive Summarization Model has been presented in this paper. Statistical features comprising of sentence-level and word-level features are employed for extracting important sentences from the given document. Word-level features include presence of common noun, proper noun, numerical information, and term frequency-inverse sentence frequency (TF-ISF) whereas sentence-level features include sentence position, sentence length and similarity to news title. A linear combination of all these features score is used to form the final score of the sentence. The ranking of sentences is then done according to the generated score and final summary is generated according to the compression ratio. In this paper, the results for five compression ratios i.e., 70%, 50%, 30%, 20% and 10% has been shown for different Rouge scores i.e., Rouge-1, Rouge-2 and Rouge-L. Also, a comparative analysis of the proposed Dogri Extractive Summarization model with other Indian Text Summarization systems like Hindi, Bengali, Punjabi, and Kannada is also presented in the paper.</p>