Loss-Based Active Learning for Neural Abstractive Summarization

2026-08-26

AI Summary

为解决高质量标注数据获取成本高问题，提出基于损失的主动学习框架LOBSTER，用于抽象摘要生成，通过优先选择与当前高损失训练样本语义相似的未标记实例来提高性能。

Abstract

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.

Problem

• abstractive summarization
• active learning
• annotated data
• computational bottlenecks
• high-quality

Innovation

• active learning
• abstractive summarization
• loss-based selection
• semantic similarity
• query speedup