George Michalopoulos

Loss-Based Active Learning for Neural Abstractive Summarization

This work proposes LOBSTER, a novel active learning framework designed specifically for abstractive summarization that improves performance by prioritizing unlabeled instances semantically similar to the model's current high-loss training examples, enabling the model to explicitly correct its specific weaknesses.