Summarization quality evaluation is a non-trivial task in text summarization.
Contemporary methods can be mainly categorized into two scenarios: (1)
reference-based: evaluating with human-labeled reference summary; (2)
reference-free: evaluating the summary consistency of the document. Recent
studies mainly focus on one of these scenarios and explore training neural
models built on PLMs to align with human criteria. However, the models from
different scenarios are optimized individually, which may result in sub-optimal
performance since they neglect the shared knowledge across different scenarios.
Besides, designing individual models for each scenario caused inconvenience to
the user. Inspired by this, we propose Unified Multi-scenario Summarization
Evaluation Model (UMSE). More specifically, we propose a perturbed prefix
tuning method to share cross-scenario knowledge between scenarios and use a
self-supervised training paradigm to optimize the model without extra human
labeling. Our UMSE is the first unified summarization evaluation framework
engaged with the ability to be used in three evaluation scenarios. Experimental
results across three typical scenarios on the benchmark dataset SummEval
indicate that our UMSE can achieve comparable performance with several existing
strong methods which are specifically designed for each scenario.

提出了一种用于三种摘要评估方案的统一多方案摘要评估模型（UMSE），其中使用扰动前缀调整方法来共享不同方案之间的信息，通过自监督训练偏向优化统一模型。在常见的标准数据集 SummEval 上进行的实验结果表明，UMSE 的性能可以与专门针对每种情况进行设计的现有强方法相媲美。