We construct Global Voices, a multilingual dataset for evaluating
cross-lingual summarization methods. We extract social-network descriptions of
Global Voices news articles to cheaply collect evaluation data for into-English
and from-English summarization in 15 languages. Especially, for the
into-English summarization task, we crowd-source a high-quality evaluation
dataset based on guidelines that emphasize accuracy, coverage, and
understandability. To ensure the quality of this dataset, we collect human
ratings to filter out bad summaries, and conduct a survey on humans, which
shows that the remaining summaries are preferred over the social-network
summaries. We study the effect of translation quality in cross-lingual
summarization, comparing a translate-then-summarize approach with several
baselines. Our results highlight the limitations of the ROUGE metric that are
overlooked in monolingual summarization. Our dataset is available for download
at this https URL .

通过构建多语言数据集 Global Voices，提取社交网络数据以对 15 种语言中的英文总结进行低成本的评估。并通过对人类进行调查和筛选，研究了翻译质量对跨语言总结的影响，同时还对 ROUGE 指标进行了限制的分析。

全球声音：自动新闻摘要中的跨界问题

Global Voices: Crossing Borders in Automatic News Summarization

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。