Lack of encyclopedic text contributors, especially on Wikipedia, makes
automated text generation for \emph{low resource (LR) languages} a critical
problem. Existing work on Wikipedia text generation has focused on
\emph{English only} where English reference articles are summarized to generate
English Wikipedia pages. But, for low-resource languages, the scarcity of
reference articles makes monolingual summarization ineffective in solving this
problem. Hence, in this work, we propose \task{}, which is the task of
cross-lingual multi-document summarization of text from multiple reference
articles, written in various languages, to generate Wikipedia-style text.
Accordingly, we contribute a benchmark dataset, \data{}, spanning $\sim$69K
Wikipedia articles covering five domains and eight languages. We harness this
dataset to train a two-stage system where the input is a set of citations and a
section title and the output is a section-specific LR summary. The proposed
system is based on a novel idea of neural unsupervised extractive summarization
to coarsely identify salient information followed by a neural abstractive model
to generate the section-specific text. Extensive experiments show that
multi-domain training is better than the multi-lingual setup on average.

本研究针对缺乏百科全书文本贡献者的问题，提出了一种基于多语言参考文章的跨语言多文档摘要生成任务，同时建立了一个基准数据集，并提出了一种基于神经网络的有监督和无监督摘要生成系统，实验表明多领域的训练要优于多语言的设置。