Text summarization has become an essential tool in managing the rapid expansion of textual information, assisting users in obtaining concise and meaningful summaries of large documents. This literature review explores three primary methodologies in text summarization: extractive, abstractive, and hybrid approaches. Extractive methods focus on selecting key sentences or phrases directly from the original text, while abstractive summarization techniques generate summaries using natural language processing models to rephrase and condense information. Hybrid summarization combines elements of both, aiming to leverage the strengths of each method. This review discusses recent advancements, challenges, and applications within these approaches, emphasizing the growing role of deep learning and language models in transforming text summarization. Finally, recommendations for future research directions are provided, highlighting potential improvements in accuracy, coherence, and scalability.