  To adapt text summarization to the multilingual world, previous work proposes
multi-lingual summarization (MLS) and cross-lingual summarization (CLS).
However, these two tasks have been studied separately due to the different
definitions, which limits the compatible and systematic research on both of
them. In this paper, we aim to unify MLS and CLS into a more general setting,
i.e., many-to-many summarization (M2MS), where a single model could process
documents in any language and generate their summaries also in any language. As
the first step towards M2MS, we conduct preliminary studies to show that M2MS
can better transfer task knowledge across different languages than MLS and CLS.
Furthermore, we propose Pisces, a pre-trained M2MS model that learns language
modeling, cross-lingual ability and summarization ability via three-stage
pre-training. Experimental results indicate that our Pisces significantly
outperforms the state-of-the-art baselines, especially in the zero-shot
directions, where there is no training data from the source-language documents
to the target-language summaries.
