DomainSum：面向抽象文本摘要中细粒度领域偏移的分层基准
DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarization
AI总结：
本文提出分层基准DomainSum，从体裁、风格和主题三个层级刻画抽象摘要中的细粒度领域偏移，并系统评估PLMs和LLMs的领域泛化能力。
摘要：
大多数关于抽象摘要的研究集中于单领域应用，往往忽视了文档之间的领域偏移如何影响摘要模型的性能与泛化能力。为解决这一问题，我们提出了DomainSum，一个旨在捕捉抽象摘要中细粒度领域偏移的分层基准。我们将这些偏移划分为三个层级：体裁、风格和主题，并通过全面的基准分析证明它们遵循分层结构。此外，我们评估了常用预训练语言模型（PLMs）和大型语言模型（LLMs）在域内和跨域设置下的领域泛化能力。
英文摘要：
Most research on abstractive summarization focuses on single-domain applications, often neglecting how domain shifts between documents affect performance and the generalization ability of summarization models. To address this issue, we introduce DomainSum, a hierarchical benchmark designed to capture fine-grained domain shifts in abstractive summarization. We categorize these shifts into three levels: genre, style, and topic, and demonstrate through comprehensive benchmark analysis that they follow a hierarchical structure. Furthermore, we evaluate the domain generalization capabilities of commonly used pre-trained language models (PLMs) and large language models (LLMs) in in-domain and cross-domain settings.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
