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arXiv Computation and Language By Michail Ioannou, Tatiana Passali, George Michalopoulos, Grigorios Tsoumakas

Loss-Based Active Learning for Neural Abstractive Summarization

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Loss-Based Active Learning for Neural Abstractive Summarization proposes LOBSTER, an active learning framework that selects unlabeled documents similar to the model’s high‑loss training examples to correct specific weaknesses. The method is tailored for abstractive summarization, addressing instability and computational bottlenecks seen in prior work. Experiments on three benchmark datasets and two backbone models show that LOBSTER matches or surpasses state‑of‑the‑art performance while speeding up query selection by up to 665×.

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