Cross-lingual summarization (CLS) is the task to produce a summary in one
particular language for a source document in a different language. Existing
methods simply divide this task into two steps: summarization and translation,
leading to the problem of error propagation. To handle that, we present an
end-to-end CLS framework, which we refer to as Neural Cross-Lingual
Summarization (NCLS), for the first time. Moreover, we propose to further
improve NCLS by incorporating two related tasks, monolingual summarization and
machine translation, into the training process of CLS under multi-task
learning. Due to the lack of supervised CLS data, we propose a round-trip
translation strategy to acquire two high-quality large-scale CLS datasets based
on existing monolingual summarization datasets. Experimental results have shown
that our NCLS achieves remarkable improvement over traditional pipeline methods
on both English-to-Chinese and Chinese-to-English CLS human-corrected test
sets. In addition, NCLS with multi-task learning can further significantly
improve the quality of generated summaries. We make our dataset and code
publicly available here: this http URL

通过多任务学习、循环翻译等方法，构建神经网络模型用于实现跨语言文本摘要，实验结果证明该模型较传统模型有显著提升。