  Cross-lingual summarization (CLS) has attracted increasing interest in recent
years due to the availability of large-scale web-mined datasets and the
advancements of multilingual language models. However, given the rareness of
naturally occurring CLS resources, the majority of datasets are forced to rely
on translation which can contain overly literal artifacts. This restricts our
ability to observe naturally occurring CLS pairs that capture organic diction,
including instances of code-switching. This alteration between languages in
mid-message is a common phenomenon in multilingual settings yet has been
largely overlooked in cross-lingual contexts due to data scarcity. To address
this gap, we introduce CroCoSum, a dataset of cross-lingual code-switched
summarization of technology news. It consists of over 24,000 English source
articles and 18,000 human-written Chinese news summaries, with more than 92% of
the summaries containing code-switched phrases. For reference, we evaluate the
performance of existing approaches including pipeline, end-to-end, and
zero-shot methods. We show that leveraging existing CLS resources as a
pretraining step does not improve performance on CroCoSum, indicating the
limited generalizability of current datasets. Finally, we discuss the
challenges of evaluating cross-lingual summarizers on code-switched generation
through qualitative error analyses.
