Text Summarization

Text summarization is an essential task in natural language processing that condenses large volumes of text into concise summaries, helping users grasp critical information efficiently. This project aims to leverage deep learning models—LSTM, Llama, and BART—on the CNN/DailyMail dataset to generate high-quality summaries that capture key elements from news articles. By combining these models, we explore both extractive and abstractive summarization methods, optimizing them to produce coherent, human-like summaries. The LSTM model enables sequential understanding of text, while Llama and BART bring transformer-based approaches for handling complex language structures. This ensemble approach seeks to balance summarization accuracy with semantic preservation, ensuring readability and information retention. The project outcomes are expected to improve information accessibility in various applications, from news aggregation to academic and industry research.

Keywords: Text Summarization, LSTM, BART, Llama, NLP, Abstractive Summarization, Extractive Summarization, Deep Learning, CNN/DailyMail, Information Retrieval