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-curated 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 resources as a pretraining
step does not improve performance on CroCoSum, indicating the limited
generalizability of existing resources. Finally, we discuss the challenges of
evaluating cross-lingual summarizers on code-switched generation through
qualitative error analyses. Our collection and code can be accessed at
this https URL.

本文介绍了 CroCoSum 数据集，这是一个跨语言代码交替技术新闻摘要的数据集。该数据集包含超过 24000 个英文来源文章和超过 18000 个中文新闻摘要，并展示了现有方法在该数据集上的表现，揭示了现有资源的有限普适性。