This project aims to perform text summarization using a pre-trained model from Hugging Face's Transformers library. The primary objective is to take long pieces of text and generate concise summaries while preserving the core information and meaning.
This project is implemented using the Pegasus model, fine-tuned with the Samsum dataset, leveraging the Transformers library from Hugging Face. The Pegasus model is specifically designed for abstractive text summarization, providing high-quality summaries that capture the essence of the input text. By fine-tuning with the Samsum dataset, which contains dialogues and conversational data, this project aims to generate concise and coherent summaries for dialogue-heavy text inputs. The implementation includes data preprocessing, model training, and evaluation to ensure the effectiveness and accuracy of the generated summaries.