  Given a document in a source language, cross-lingual summarization (CLS) aims
at generating a concise summary in a different target language. Unlike
monolingual summarization (MS), naturally occurring source-language documents
paired with target-language summaries are rare. To collect large-scale CLS
data, existing datasets typically involve translation in their creation.
However, the translated text is distinguished from the text originally written
in that language, i.e., translationese. In this paper, we first confirm that
different approaches of constructing CLS datasets will lead to different
degrees of translationese. Then we systematically investigate how
translationese affects CLS model evaluation and performance when it appears in
source documents or target summaries. In detail, we find that (1) the
translationese in documents or summaries of test sets might lead to the
discrepancy between human judgment and automatic evaluation; (2) the
translationese in training sets would harm model performance in real-world
applications; (3) though machine-translated documents involve translationese,
they are very useful for building CLS systems on low-resource languages under
specific training strategies. Lastly, we give suggestions for future CLS
research including dataset and model developments. We hope that our work could
let researchers notice the phenomenon of translationese in CLS and take it into
account in the future.
