TEXT SUMMARIZATION USING NLP

Chetana Varagantham, J.Srinija Reddy, Uday Yelleni,, Madhumitha Kotha, P.Venkateswara Rao
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Abstract

This Project represents the work related to Text Summarization. In this paper, we present a framework for summarizing the huge information. The proposed framework depends on highlight extraction from the internet, utilizing both morphological elements and semantic data. Presently, where huge information is available on the internet, it is most important to provide improved ways to extract the information quickly and most efficiently. It is very difficult for human beings to manually extract the summary of a large document of text. There are plenty of text materials available on the internet. So, there is a problem of searching for related documents from the number of documents available and absorbing related information from it. In essence to figure out the previous issues, automatic text summarization is very much necessary. Text Summarization is the process of identifying the most important and meaningful information in an input document or set of related input documents and compressing all the inputs into a shorter version while maintaining its overall objectives.
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使用NLP的文本摘要
本项目代表了与文本摘要相关的工作。在本文中,我们提出了一个对海量信息进行汇总的框架。提出的框架依赖于从互联网中提取亮点,同时利用形态元素和语义数据。目前,在互联网上有大量的信息,提供改进的方法来快速有效地提取信息是最重要的。人工提取大型文本文档的摘要是非常困难的。网上有大量的文字资料。因此,从现有的文档数量中搜索相关文档并从中吸收相关信息是一个问题。从本质上讲,要解决前面的问题,自动文本摘要是非常有必要的。文本摘要是在一个输入文档或一组相关输入文档中识别最重要和最有意义的信息,并在保持其总体目标的同时将所有输入压缩成更短的版本的过程。
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