  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 https://github.com/krystalan/ClidSum.
