Overview This project focuses on the development of a machine learning model to automatically generate news titles from article abstracts using the New York Times (NYT) dataset. By leveraging advanced natural language processing (NLP) techniques, the model aims to produce concise, relevant, and engaging titles that capture the essence of the articles.
Features Data Preprocessing: Implements techniques for cleaning and preparing the NYT dataset for training. Model Training: Utilizes a combination of LSTM/GRU networks with attention mechanisms to understand the context and semantics of the abstracts. Title Generation: Employs a pointer-generator network for effective summarization and title generation, allowing for both content extraction and abstraction. Evaluation: Adopts metrics like BLEU and ROUGE for assessing the performance of the generated titles against human-written titles.