基于损失的神经抽象摘要生成主动学习
Loss-Based Active Learning for Neural Abstractive Summarization
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- School of Informatics（信息学院）
- Aristotle University of Thessaloniki（亚里士多德塞萨洛尼基大学）
- Microsoft（微软公司）
机构由 AI 辅助整理，请以论文原文为准。
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中文总结 AI 辅助
针对神经抽象摘要生成标注数据获取成本高的问题，提出LOBSTER主动学习框架，在三个数据集和两个模型上验证其性能优于或相当SOTA，且查询选择速度最高提升665倍
中文摘要 AI 辅助
微调抽象摘要生成模型需要高质量的标注数据，但获取这类语料成本高昂且耗时，因为需要人工标注员阅读并理解长文档以生成准确摘要。主动学习通过仅选择最具信息性的实例进行标注，使模型能用少得多的标签达到有竞争力的结果。然而，主动学习在摘要生成中的应用仍未被充分探索，现有研究常存在不稳定性和显著的计算瓶颈。为克服这些挑战，我们提出LOBSTER（LOss-BaSed acTivE leaRning，基于损失的主动学习），一种专为抽象摘要生成设计的新型主动学习框架。LOBSTER通过优先选择与模型当前高损失训练实例语义相似的未标注实例来提升性能，使模型能明确修正其特定弱点。我们在三个基准数据集和两个摘要生成主干模型上的实证评估表明，LOBSTER始终与当前最先进方法相当或更优，同时实现了最高达665倍的查询选择加速。
英文摘要
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.
