Text Summarization Techniques - Extractive vs. Abstractive: Exploring extractive and abstractive text summarization techniques for generating concise summaries from longer text documents or articles

Abstract

Text summarization plays a crucial role in condensing large amounts of text into shorter, more manageable summaries. This paper provides an in-depth analysis of extractive and abstractive text summarization techniques, comparing their strengths, weaknesses, and applications. Extractive summarization involves selecting important sentences or phrases from the original text, while abstractive summarization involves generating new sentences to capture the essence of the original text. We discuss various algorithms and models used in both approaches, including TF-IDF, LSA, TextRank, and neural network-based models. Additionally, we examine evaluation metrics and challenges in text summarization, such as maintaining coherence and preserving important information. Finally, we discuss potential future directions in text summarization research, including the integration of machine learning and natural language processing techniques to improve summarization quality and efficiency.