Text Intelligence Engine: ‘Unleashing the power of Summarization with Machine Learning.’

Abstract

In the era of exponential growth in online data, the proliferation of online information necessitates effective strategies for text summarization to distill vast amounts of data into concise, digestible insights. This paper presents a comprehensive investigation into text summarization methodologies within the context of a Text Intelligence Engine project, with a primary emphasis on the Text Rank algorithm. Leveraging the principles of graph-based ranking, our approach harnesses the power of Text Rank to identify key concepts and generate coherent summaries. Through meticulous experimentation and rigorous evaluation, we demonstrate the robustness and effectiveness of Text Rank in extracting salient information from diverse textual sources. Furthermore, we explore the integration of Text Rank with machine learning techniques to enhance summarization performance. By elucidating the theoretical foundations and practical applications of Text Rank, this paper contributes to the advancement of text intelligence systems, offering valuable insights and guidelines for researchers and practitioners in the field. Keywords -Natural Language Processing, NLKT, Text Rank Algorithm, Supervised Learning, Random Algorithm, Text summarization, Extractive summarization, Abstractive summarization, Machine learning, SpaCy, Neural networks, Rhetorical structure theory