An Empirical Study On Text Summarization Techniques By Integrating NLP With Machine And Deep Learning Techniques

Abstract

From the past few decades, data storage in multiple sources is getting more attention. Due to either time constraints in the current scenario or busy life in the co-operate world irrespective of the age factor, people did attract towards reading the in short news to get acquainted with the national and international news especially in their regional languages. So, the proposed paper has conducted an empirical study on the regional language "Telugu," summarization that generates a brief note of huge texts stored in multiple databases. In the early days, text summarization does perform with traditional NLP approaches, with the advancement of Artificial Intelligence; it has spread its wings to the world of NLP also, to summarize the text smartly. Smart Text Summarization technique can reduce the time and work a lot for any human to understand the exact purpose of that document. However, the real complexity arises while developing such an abstract summary by choosing the required words or phrases that fit the whole document. Some kinds of texts also were used to find its sensitivity which is frequently used in social media texts, reviews, and e-commerce sites to know the exact view of the customer or the person