IJSAR, 6(5), 2019; 20-30 20 International Journal of Sciences & Applied Research www.ijsar.in An Extractive Approach for English Text Summarization Kanchan D. Patil 1 , Sandip A. Patil 2 , Yogesh S. Deshmukh 1 * 1 Department of Information Technology, Sanjivani College of Engineering, Kopargaon, India. 2 Department of Computer Engineering, Sanjivani College of Engineering, Kopargaon, India. Corresponding author: *Yogesh S. Deshmukh, Department of Information Technology, Sanjivani College of Engineering, Kopargaon, India. _____________________________________________________________________________________________ Abstract Natural-language processing (NLP) is a vast area of computer science, artificial intelligence concerned with the interactions between computers and human languages. The “natural language” means a language that is used for daily communication by humans. The development of NLP applications is challenging because computers traditionally require humans to "speak" to them in a programming language that is precise, unambiguous and highly structure. Text summarization is one of the research area of NLP which gives us meaningful and short description of the vast text documents using different NLP tools and techniques. Nowadays, as we are dealing with huge amount of Digital data it is necessary to have automatic Text Summarization Techniques. Text summarization specifically classified into two major categories as Extractive Text Summarization and Abstractive Text Summarization. This paper focuses on different Extractive Text Summarization Techniques used for Indian Languages. Keywords: NLP, Text Summerization; Extractive Text Summerization, Abstractive Text Summerization Introduction Nowadays we are dealing with the large amount of Digital data on the Internet. If you want to search some information on search engine for example ‘Text’, that web gives you large documents of information that consists of your search word ‘Text’. The information may be relevant to your search are may not be. As well the contents may be duplicate. It is difficult for human to read all the documents completely [1, 2, 3]. So, we require the automatic Text summarization. According to Radef et al. [4] a summary is defined as “a text that is produced from one or more texts, that conveys important information in the original text(s), and that is no longer than half of the original text(s) and usually, significantly less than that”. Automatic summarization can be defined as a process of shortening a text document with software, in order to create a summary with the major points of the original document. These Automatic summarization tools and Techniques help human to read and understand the document in short time. Automatic Text summarization is used in various applications such as Search Engine, Articles, Newspapers, Research Abstract etc[5]. Research in the area of Text summarization started in 1950’s but till now no system is available that summarizes the text like human. Some are focusing on