  Recently, a large number of tuning strategies have been proposed to adapt
pre-trained language models to downstream tasks. In this paper, we perform an
extensive empirical evaluation of various tuning strategies for multilingual
learning, particularly in the context of text summarization. Specifically, we
explore the relative advantages of three families of multilingual tuning
strategies (a total of five models) and empirically evaluate them for
summarization over 45 languages. Experimentally, we not only established a new
state-of-the-art on the XL-Sum dataset but also derive a series of observations
that hopefully can provide hints for future research on the design of
multilingual tuning strategies.
