The recently introduced Controlled Text Reduction (CTR) task isolates the text generation step within typical summarization-style tasks. It does so by challenging models to generate coherent text conforming to pre-selected content within the input text ("highlights"). This framing enables increased modularity in summarization-like tasks, allowing to couple a single CTR model with various content-selection setups and modules. However, there are currently no reliable CTR models, while the performance of the existing baseline for the task is mediocre, falling short of practical utility. Here, we address this gap by introducing a high-quality, open-source CTR model that tackles two prior key limitations: inadequate enforcement of the content-preservation constraint, and suboptimal silver training data. Addressing these, we amplify the content-preservation constraint in both training, via RL, and inference, via a controlled decoding strategy. Further, we substantially improve the silver training data quality via GPT-4 distillation. Overall, pairing the distilled dataset with the highlight-adherence strategies yields marked gains over the current baseline, of up to 30 ROUGE-L points, providing a reliable CTR model for downstream use.

翻译：近期提出的受控文本缩减任务将典型摘要类任务中的文本生成步骤独立出来。该任务通过要求模型根据输入文本中的预选内容生成连贯文本，从而提高了摘要相关任务的模块化程度，使单一受控文本缩减模型可与不同的内容选择机制和模块结合使用。然而，目前尚缺乏可靠的受控文本缩减模型，现有基线模型的性能平庸且不具实用价值。本研究通过开发一个高质量开源受控文本缩减模型填补这一空白，针对性解决两个核心缺陷：内容保留约束的强制执行不足，以及次优的银标准训练数据。针对前者，我们在训练阶段通过强化学习增强约束，在推理阶段采用受控解码策略；针对后者，则利用GPT-4蒸馏技术显著提升银标准训练数据质量。综合实验表明，将蒸馏数据集与高亮文本遵循策略相结合，相比现有基线模型可获得最高30个ROUGE-L点的性能提升，为下游应用提供了可靠的受控文本缩减模型。