用损失值选关键样本，高效提升摘要模型性能
Loss-Based Active Learning for Neural Abstractive Summarization
- 根据模型高损失样本选择相似未标注数据
- 在3个数据集上超越现有方法，提速超665倍
- 适合资源有限但需高质量摘要的场景
微调抽象式摘要模型需要高质量标注数据，但人工生成准确摘要成本高、耗时长。主动学习通过挑选最有效的样本进行标注，可显著减少标签需求。然而当前主动学习在摘要任务中研究不足，且常存在不稳定和计算瓶颈问题。为此，我们提出专为抽象摘要设计的LOBSTER（基于损失的主动学习）框架，通过优先选择与模型当前高损失样本语义相似的未标注实例，使模型能针对性修正弱点。在三个基准数据集和两种骨干模型上的实验表明，LOBSTER在性能上持续匹配或超越现有最优方法，同时查询选择速度最高提升665倍。
原文摘要 · Abstract (English)
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.
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