Recent advances in summarization research focus on improving summary quality across multiple criteria, such as completeness, conciseness, and faithfulness, by jointly optimizing these dimensions. However, these efforts largely overlook the challenge of controlling summary generation with respect to individual criteria, especially in the presence of their inherent trade-offs. For example, enhancing conciseness can compromise completeness, and vice versa. In this work, we address this gap by proposing a loss function that aligns model outputs with fine-grained, model-based evaluation scores (e.g., from FineSurE), enabling both improvement in summary quality and dimension-specific control. Our approach improves the overall quality of summaries while maintaining the ability to selectively prioritize one criterion over others. Experiments on three pretrained models (LLaMA, Qwen, and Mistral) demonstrate that our method achieves performance comparable to state-of-the-art summarizers, while uniquely offering strong controllability over individual quality dimensions.

翻译：近期摘要生成研究的进展聚焦于通过联合优化多个维度来提升摘要质量，如完整性、简洁性与忠实性。然而，这些工作大多忽视了针对单个标准控制摘要生成这一挑战，特别是在各标准存在固有权衡的情况下。例如，提升简洁性可能损害完整性，反之亦然。本研究通过提出一种损失函数来填补这一空白，该损失函数使模型输出与基于模型的细粒度评估分数（如FineSurE）对齐，从而既提升摘要质量，又实现维度特定控制。我们的方法在保持按需优先强调某一标准的能力的同时，提升了摘要的整体质量。在三个预训练模型（LLaMA、Qwen和Mistral）上的实验表明，该方法达到了与最先进摘要生成器相当的性能，同时独特地提供了对单个质量维度的强可控性。