We present CrossSum, a large-scale cross-lingual abstractive summarization
dataset comprising 1.7 million article-summary samples in 1500+ language pairs.
We create CrossSum by aligning identical articles written in different
languages via cross-lingual retrieval from a multilingual summarization
dataset. We propose a multi-stage data sampling algorithm to effectively train
a cross-lingual summarization model capable of summarizing an article in any
target language. We also propose LaSE, a new metric for automatically
evaluating model-generated summaries and showing a strong correlation with
ROUGE. Performance on ROUGE and LaSE indicate that pretrained models fine-tuned
on CrossSum consistently outperform baseline models, even when the source and
target language pairs are linguistically distant. To the best of our knowledge,
CrossSum is the largest cross-lingual summarization dataset and the first-ever
that does not rely solely on English as the pivot language. We are releasing
the dataset, alignment and training scripts, and the models to spur future
research on cross-lingual abstractive summarization. The resources can be found
at this https URL

通过跨语言检索，提出跨语言摘要数据集 CrossSum 及其训练模型，表现出优异的 ROUGE 和 LaSE 性能，成为目前最大的跨语言摘要数据集