Effective Techniques in News Text Summarization: An Evaluation of Extractive, Abstractive and Hybrid Approaches

With the growing volume of online news content, automatic text summarization has become an essential tool for processing and delivering concise information. This paper provides an in-depth analysis of various techniques used in news text summarization, focusing on their effectiveness and applicability in real-world scenarios. The study compares extractive, abstractive, and hybrid summarization methods, exploring their advantages and limitations. Key advancements in Natural Language Processing (NLP) technologies, such as transformer models, are discussed for their role in improving summarization quality. The paper also highlights the challenges associated with news text summarization, such as handling diverse linguistic structures, preserving factual accuracy, and avoiding redundancy. Through experiments with large-scale news datasets, the research evaluates the performance of state-ofthe- art models, offering insights into their potential to enhance user experience by providing accurate and meaningful news summaries. The findings contribute to the ongoing development of more efficient and accurate news summarization systems.