An Algorithm for Summarization of Paragraph Up to One Third with the Help of Cue Words Comparison

Abstract

Abstract-In the fast growing information era utility of technology are more precise than completing the assignment manually. The digital information technology creates a knowledge-based society with high-tech global economy which spreads over and influence the corporate and service sector to operate in more efficient and convenient way. Here an attempt was made on Extract Technology based on research. In this technology data could be refined and sourced with certainty and relevance. The application of artificial intelligence matched with the theories of machine learning would prove to be very effective. Sometime summarization of paragraph required rather than page or pages. So, Auto Summarization Model is an agnostic content summarization technology that automatically parses news, information, documents and many more into relevant and contextually accurate abbreviated summaries. This is a concept to convert a whole paragraph into one third. The Auto summarization technology reads a document, much better way than manually prepared, where, keywords and key phrases accurately weighted as they are found in the document, text or web page.

Proposing a Keyword Extraction Scheme based on Standard Deviation, Frequency and Conceptual Relation of the Words

“…These applications include: automatic text summarization [5,6], information retrieval [7], text classification [8], fast and accurate searching of texts in web [9] and automatic indexing [10,3]. Keyword extraction methods are the basis schemes for all of these applications and the right choosing of essential keywords is the main purpose of this research.…”