While summarization has been extensively researched in natural language
processing (NLP), cross-lingual cross-temporal summarization (CLCTS) is a
largely unexplored area that has the potential to improve cross-cultural
accessibility, information sharing, and understanding. This paper
comprehensively addresses the CLCTS task, including dataset creation, modeling,
and evaluation. We build the first CLCTS corpus, leveraging historical fictive
texts and Wikipedia summaries in English and German, and examine the
effectiveness of popular transformer end-to-end models with different
intermediate task finetuning tasks. Additionally, we explore the potential of
ChatGPT for CLCTS as a summarizer and an evaluator. Overall, we report
evaluations from humans, ChatGPT, and several recent automatic evaluation
metrics where we find our intermediate task finetuned end-to-end models
generate bad to moderate quality summaries; ChatGPT as a summarizer (without
any finetuning) provides moderate to good quality outputs and as an evaluator
correlates moderately with human evaluations though it is prone to giving lower
scores. ChatGPT also seems to be very adept at normalizing historical text. We
finally test ChatGPT in a scenario with adversarially attacked and unseen
source documents and find that ChatGPT is better at omission and entity swap
than negating against its prior knowledge.

本文探讨如何应用跨语言和跨时期的文字连线技术在信息总结中的应用，并通过构建数据集、建模和评估得出结论：通过中间任务微调的端到端模型的总结质量一般；ChatGPT（无需微调）的总结质量良好，并与人类评分中相关；在对抗攻击的情况下，ChatGPT 在省略和实体替换方面的表现更好。