• Bibhesh Kumar
• Durgesh Singh
• Vivek Mishra
• Krishna Mohan Yadav

This paper investigates the application of natural language processing (NLP) techniques for data summarization. With the increasing amount of text data available in various fields, there is a growing need to automatically generate summaries that retain the most important information. This paper explores different techniques for text summarization, including extractive and abstractive approaches. We also examine the various NLP tools and libraries that can be used for data summarization, such as NLTK, SpaCy, and Gensim. In addition, we discuss challenges and constraints of current approaches and give insights into potential future research directions. The findings of this research suggest that NLP-based data summarization can be an effective solution for handling large volumes of text data and has significant potential for applications in various domains, including journalism, legal documents, and medical reports. We also discuss some of the future directions and open problems in this field.

Automatic summarization,Extractive,frequency-based, Natural Language Processing,Latent Semantic Analysis.

"Data Summerization Using NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.i58-i63, May-2023, Available :http://www.jetir.org/papers/JETIR2305837.pdf

"Data Summerization Using NLP", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppi58-i63, May-2023, Available at : http://www.jetir.org/papers/JETIR2305837.pdf