  Cross-lingual summarization (XLS) generates summaries in a language different
from that of the input documents (e.g., English to Spanish), allowing speakers
of the target language to gain a concise view of their content. In the present
day, the predominant approach to this task is to take a performing, pretrained
multilingual language model (LM) and fine-tune it for XLS on the language pairs
of interest. However, the scarcity of fine-tuning samples makes this approach
challenging in some cases. For this reason, in this paper we propose revisiting
the summarize-and-translate pipeline, where the summarization and translation
tasks are performed in a sequence. This approach allows reusing the many,
publicly-available resources for monolingual summarization and translation,
obtaining a very competitive zero-shot performance. In addition, the proposed
pipeline is completely differentiable end-to-end, allowing it to take advantage
of few-shot fine-tuning, where available. Experiments over two contemporary and
widely adopted XLS datasets (CrossSum and WikiLingua) have shown the remarkable
zero-shot performance of the proposed approach, and also its strong few-shot
performance compared to an equivalent multilingual LM baseline, that the
proposed approach has been able to outperform in many languages with only 10%
of the fine-tuning samples.
