Text summarization using deep learning is a prominent research area in natural language processing that focuses on automatically generating concise and informative summaries from large text corpora. Early approaches relied on extractive methods that selected key sentences based on statistical or linguistic features, while abstractive methods aimed to generate novel summaries using sequence-to-sequence models with recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Recent advances leverage attention mechanisms, transformer-based architectures such as BERT, GPT, and T5, and reinforcement learning to optimize summary coherence, relevance, and informativeness. Hybrid models combining extractive and abstractive techniques, as well as graph-based methods, have further enhanced performance. Applications of text summarization span news aggregation, scientific literature review, legal document analysis, question answering, and content recommendation. Current research also explores multilingual summarization, domain adaptation, low-resource scenarios, and evaluation metrics beyond traditional ROUGE scores, establishing deep learning-based text summarization as a key technology for efficient knowledge extraction and information retrieval.