  Parallel cross-lingual summarization data is scarce, requiring models to
better use the limited available cross-lingual resources. Existing methods to
do so often adopt sequence-to-sequence networks with multi-task frameworks.
Such approaches apply multiple decoders, each of which is utilized for a
specific task. However, these independent decoders share no parameters, hence
fail to capture the relationships between the discrete phrases of summaries in
different languages, breaking the connections in order to transfer the
knowledge of the high-resource languages to low-resource languages. To bridge
these connections, we propose a novel Multi-Task framework for Cross-Lingual
Abstractive Summarization (MCLAS) in a low-resource setting. Employing one
unified decoder to generate the sequential concatenation of monolingual and
cross-lingual summaries, MCLAS makes the monolingual summarization task a
prerequisite of the cross-lingual summarization (CLS) task. In this way, the
shared decoder learns interactions involving alignments and summary patterns
across languages, which encourages attaining knowledge transfer. Experiments on
two CLS datasets demonstrate that our model significantly outperforms three
baseline models in both low-resource and full-dataset scenarios. Moreover,
in-depth analysis on the generated summaries and attention heads verifies that
interactions are learned well using MCLAS, which benefits the CLS task under
limited parallel resources.
