We present ClidSum, a benchmark dataset for building cross-lingual
summarization systems on dialogue documents. It consists of 67k+ dialogue
documents from two subsets (i.e., SAMSum and MediaSum) and 112k+ annotated
summaries in different target languages. Based on the proposed ClidSum, we
introduce two benchmark settings for supervised and semi-supervised scenarios,
respectively. We then build various baseline systems in different paradigms
(pipeline and end-to-end) and conduct extensive experiments on ClidSum to
provide deeper analyses. Furthermore, we propose mDialBART which extends
mBART-50 (a multi-lingual BART) via further pre-training. The multiple
objectives used in the further pre-training stage help the pre-trained model
capture the structural characteristics as well as important content in
dialogues and the transformation from source to the target language.
Experimental results show the superiority of mDialBART, as an end-to-end model,
outperforms strong pipeline models on ClidSum. Finally, we discuss specific
challenges that current approaches faced with this task and give multiple
promising directions for future research. We have released the dataset and code
at this https URL.

介绍了一个用于在对话文档上建立跨语言摘要系统的基准数据集 ClidSum，该数据集包含来自两个子集（即 SAMSum 和 MediaSum）的 67k + 对话文档和 112k + 不同目标语言的注释摘要。 并在基于 ClidSum 提出了两个监督和半监督场景的基准设置，接着构建了各种基线系统和在 ClidSum 上进行了广泛的实验来提供更深入的分析。最后提出了 mDialBART，该模型通过进一步预训练扩展了 mBART-50（一种多语言 BART），在实验结果中优于 ClidSum 上的强流水线模型，提出了这项任务当前面临的特定挑战并为未来研究提供了多个有前途的方向。