Text Summarization in Python Using Natural Language Processing

Abstract

Broad amount of information is accessible online on the internet. The purpose of search engines like Google and Bing is to retrieve data from archives.The actual results have not yet been reached due to the rapid growth in number of computerized data. Subsequently, the default summary is much needed. The default summary takes several pages as insert and output an abridged version, saving both information and time.

In the current age, where immense amount of information is present online, it is much important to give the improved mechanism to draw out the information quickly and more efficiently. It is very tough that people can draw out a summary of large text documents on their own. So, there is a problem in inquiring for appropriate document from all the documents available, and consuming relevant information from it. In order to solve the problem above, the automatic text summarization is very much needed. The practice of extracting the most significant and meaningful information from a document or group of related papers and condensing it while retaining its overall meaning is known as text summarization.

Natural Language Processing is defined as the capability of a computer program to acknowledge human language when speaking and writing a language. Natural Language Processing is one of the key components of practical wisdom. It has many applications in various fields and we use it in many real-world applications such as Business Intelligence medical research and much more. We have been using Natural Language Processing for 50 years now.

The research was done in one volume and accumulated in many volumes. This research is centered on an idea build on the abbreviations of text summaries.