A Novel NLP Application to Automatically Generate Text Extraction Concepts from Textual Descriptions

Text summarization has become a sophisticated approach for the quick searching, automatic sorting, abstract generating etc., to the large amount of data. The involvement of complete study of passage and extra time is needed to generate the essence of any content. Subsequently, Natural Language Processing is an information extraction approach to automatically extract the artifacts from the textual descriptions. Moreover, NLP is often applied to generate the various element of concerns like essential terms, class models, test cases from the initial Textual descriptions. However, it is usually required to study complete passage to extract relevant information from textual content that makes this process time consuming. This research article proposed a novel and fully automatic NLP methodology to generate crux from content. As a part of research, a tool Efficient Text Summary from Text (ETST) is developed. Research authentication is achieved through the implementation of two state-of-the-art case studies. The experimental outcome proved that our suggested Natural Language Processing methodology is novel and fully automatic and is also useful for the future researchers of this domain.