This project implements neural abstractive summarization by leveraging BERT’s pretrained encoder via transfer learning. The architecture replaces the encoder with BERT and trains a Transformer decoder from scratch, avoiding the sequential processing bottleneck of LSTM-based models. The system includes preprocessing, training, and inference components with a Flask server for serving predictions.