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This paper introduces LOBSTER, a novel active learning framework tailored for neural abstractive summarization, which addresses the challenges of high-quality annotated data acquisition. By prioritizing unlabeled instances that are semantically similar to high-loss examples, LOBSTER enables models to efficiently correct their weaknesses with fewer annotations. Empirical evaluations show that LOBSTER not only matches but often surpasses state-of-the-art methods while achieving a remarkable query selection speedup of up to 665x.
Active learning can dramatically reduce the annotation burden in summarization tasks, with LOBSTER achieving up to 665x faster query selection without sacrificing performance.
Fine-tuning abstractive summarization models requires high-quality annotated data. However, obtaining such corpora is expensive and time-consuming, as it requires human annotators to read and comprehend long documents to create accurate summaries. Active learning mitigates this issue by selecting only the most informative instances for annotation, allowing models to achieve competitive results with significantly fewer labels. However, the application of active learning to summarization remains under-explored, and existing studies often suffer from instability and significant computational bottlenecks. To overcome these challenges, we propose LOBSTER (LOss-BaSed acTivE leaRning), a novel active learning framework designed specifically for abstractive summarization. LOBSTER 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. Our empirical evaluation across three benchmark datasets and two summarization backbone models demonstrates that LOBSTER consistently matches or outperforms current state-of-the-art approaches while achieving a query selection speedup of up to 665x.
Microsoft Research