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 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 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 that 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 but is prone to giving lower scores. ChatGPT also seems very adept at normalizing historical text and outperforms context-unaware spelling normalization tools such as Norma. We finally test ChatGPT in a scenario with adversarially attacked and unseen source documents and find that ChatGPT profits from its prior knowledge to a certain degree, with better performances for omission and entity swap than negation against its prior knowledge. This benefit inflates its assessed quality as ChatGPT performs slightly worse for unseen source documents compared to seen documents. We additionally introspect our models' performances to find that longer, older and more complex source texts (all of which are more characteristic for historical language variants) are harder to summarize for all models, indicating the difficulty of the CLCTS task.

翻译：尽管摘要生成在自然语言处理（NLP）领域已得到广泛研究，但跨语言跨时间摘要（CLCTS）仍是一个鲜少探索的方向，其潜在价值在于改善跨文化可访问性与理解力。本文全面探讨了CLCTS任务，涵盖数据集构建、建模与评估。我们利用英语和德语的历史虚构文本及维基百科摘要，构建了首个CLCTS语料库，并检验了不同中间微调任务下主流Transformer端到端模型的有效性。此外，我们探索了ChatGPT作为摘要生成器与评估器在CLCTS中的潜力。总体而言，我们报告了来自人工、ChatGPT及多种近期自动评估指标的评估结果，发现：经过中间任务微调的端到端模型生成的摘要质量介于较差至中等之间；ChatGPT作为摘要生成器（未经任何微调）可提供中等至良好质量的输出，而作为评估器时虽与人工评估具有中等相关性，但倾向于给出较低分数。ChatGPT还表现出对历史文本的出色规范化能力，其性能优于Norma等上下文无关拼写规范化工具。我们最终测试了ChatGPT在对抗攻击与未见源文档场景下的表现，发现ChatGPT在一定程度上受益于先验知识，相较于违背其先验知识的否定操作，其在遗漏和实体替换场景中表现更优。这一优势夸大了其评估质量——ChatGPT对未见源文档的处理略逊于已见文档。我们进一步深入分析模型性能，发现所有模型对更长、更古老且更复杂的源文本（这些特征更符合历史语言变体）的摘要生成均更为困难，凸显了CLCTS任务的挑战性。