  The goal of the cross-lingual summarization (CLS) is to convert a document in
one language (e.g., English) to a summary in another one (e.g., Chinese).
Essentially, the CLS task is the combination of machine translation (MT) and
monolingual summarization (MS), and thus there exists the hierarchical
relationship between MT\&MS and CLS. Existing studies on CLS mainly focus on
utilizing pipeline methods or jointly training an end-to-end model through an
auxiliary MT or MS objective. However, it is very challenging for the model to
directly conduct CLS as it requires both the abilities to translate and
summarize. To address this issue, we propose a hierarchical model for the CLS
task, based on the conditional variational auto-encoder. The hierarchical model
contains two kinds of latent variables at the local and global levels,
respectively. At the local level, there are two latent variables, one for
translation and the other for summarization. As for the global level, there is
another latent variable for cross-lingual summarization conditioned on the two
local-level variables. Experiments on two language directions (English-Chinese)
verify the effectiveness and superiority of the proposed approach. In addition,
we show that our model is able to generate better cross-lingual summaries than
comparison models in the few-shot setting.
