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 (1) build the first CLCTS corpus with 328 instances for hDe-En (extended version with 455 instances) and 289 for hEn-De (extended version with 501 instances), leveraging historical fiction texts and Wikipedia summaries in English and German; (2) examine the effectiveness of popular transformer end-to-end models with different intermediate finetuning tasks; (3) explore the potential of GPT-3.5 as a summarizer; (4) report evaluations from humans, GPT-4, and several recent automatic evaluation metrics. Our results indicate that intermediate task finetuned end-to-end models generate bad to moderate quality summaries while GPT-3.5, as a zero-shot summarizer, provides moderate to good quality outputs. GPT-3.5 also seems very adept at normalizing historical text. To assess data contamination in GPT-3.5, we design an adversarial attack scheme in which we find that GPT-3.5 performs slightly worse for unseen source documents compared to seen documents. Moreover, it sometimes hallucinates when the source sentences are inverted against its prior knowledge with a summarization accuracy of 0.67 for plot omission, 0.71 for entity swap, and 0.53 for plot negation. Overall, our regression results of model performances suggest 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领域已得到广泛研究，但跨语言跨时间摘要作为一个具有提升跨文化可及性与理解潜力的方向，目前仍属尚未充分探索的领域。本文系统性地研究了CLCTS任务，涵盖数据集构建、建模与评估三个维度。我们（1）利用英文和德文的历史小说文本及维基百科摘要，构建了首个CLCTS语料库，包含328个hDe-En实例（扩展版含455个实例）和289个hEn-De实例（扩展版含501个实例）；（2）探究了采用不同中间微调任务的流行Transformer端到端模型的有效性；（3）探索了GPT-3.5作为摘要生成器的潜力；（4）综合汇报了人工评估、GPT-4评估及多种近期自动评估指标的评测结果。实验结果表明：经过中间任务微调的端到端模型仅能生成较差至中等质量的摘要，而GPT-3.5作为零样本摘要生成器可产出中等至良好质量的摘要。GPT-3.5在历史文本规范化处理方面也表现出较强能力。为评估GPT-3.5的数据污染情况，我们设计了对抗性攻击方案，发现相较于已见文档，GPT-3.5对未见源文档的处理性能略有下降。此外，当源文句与其先验知识相悖时，模型偶尔会产生幻觉：在情节省略、实体替换和情节否定三种攻击场景下的摘要准确率分别为0.67、0.71和0.53。总体而言，模型性能的回归分析表明，源文本长度更长、年代更久远、复杂度更高（这些特征在历史语言变体中更为典型）时，所有模型的摘要生成难度都会增加，这印证了CLCTS任务的内在挑战性。