http://www.iaeme.com/IJARET/index.asp 530 editor@iaeme.com International Journal of Advanced Research in Engineering and Technology (IJARET) Volume 12, Issue 1, January 2021, pp. 530-538, Article ID: IJARET_12_01_049 Available online at http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=12&IType=1 Journal Impact Factor (2020): 10.9475 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6480 and ISSN Online: 0976-6499 DOI: 10.34218/IJARET.12.1.2021.049 © IAEME Publication Scopus Indexed TEXT SUMMARIZATION FOR INDIAN LANGUAGES: A SURVEY Kishore Kumar Mamidala Associate Professor, Department of Computer Science and Engineering Vivekananda Institute of Technology and Science, Karimnagar, India Suresh Kumar Sanampudi Assistant Professor and Head of Department of Information Technology Jawaharlal Nehru Technological University Hyderabad, Telangana, India ABSTRACT With the increasing amount of huge data availability on the internet, the need for automatic text summarization has emerged in the recent past. Text summarization methods generate summaries of the relevant information from original content. Text summarization methods are two types abstractive and extractive. For English, numerous text summarization techniques exist in the literature. But for Indian languages, there are only a few techniques developed. This paper presents a survey and analysis of text summarization methods developed for Indian languages—the challenges involved in summarizing Indian language documents. Merits and demerits of the existing techniques are listed. This paper also investigates which method is ideal for summarizing documents in Indian languages. Keywords: Natural Language Processing; Text Summarization; Extractive summarization; Statistical Methods; Machine Learning. Cite this Article: Kishore Kumar Mamidala and Suresh Kumar Sanampudi, Text Summarization for Indian Languages: A Survey, International Journal of Advanced Research in Engineering and Technology, 12(1), 2021, pp. 530-538. http://www.iaeme.com/IJARET/issues.asp?JType=IJARET&VType=12&IType=1 1. INTRODUCTION Text Summarization is a method of extracting or deriving the abstract of the original information [2]. In Mani and Maybury [3], text summarization distills the essential information from a text concerning a task and user. The summary generated consists of 20% to 30% of the original content [4]. Extractive and Abstractive methods are two broad classifications of Text summarization. Abstractive summarization methods use natural language generation tools to