Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{155601,
        author = {Manasaveerashyva Y N and Prathibha B S},
        title = {A survey on Text summarization using natural language processing},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {9},
        number = {1},
        pages = {1514-1519},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=155601},
        abstract = {Text Summarization is a Natural Language Processing (NLP) method that extracts and collects data fromthe source and summarizes it. Text summarization has become a requirement for many applications since manually summarizing vast amounts of information is difficult, especially with the expanding magnitude of data. Financial research, search engine optimization, media monitoring, question-answering bots, and document analysis all benefit from text summarization. This paper extensively addresses several summarizing strategies depending on intent, volume ofdata, and outcome. Our aim is to evaluate and convey an abstract viewpoint of the present scenario research work fortext summarization.},
        keywords = {Natural Language Processing, Text Summarization, Abstractive Summary, Extractive Summary.},
        month = {},
        }