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.

翻译：维基百科等百科类文本的贡献者匮乏，尤其是对低资源语言而言，使得自动化文本生成成为关键问题。现有维基百科文本生成研究主要聚焦于英语，即通过摘要英文参考文章生成英文维基百科页面。但对于低资源语言而言，参考文章的稀缺使得单语摘要方法难以有效解决该问题。因此，本文提出跨语言多文档摘要任务——对多篇来自不同语言的参考文章进行摘要以生成维基百科风格的文本。我们构建了一个基准数据集，涵盖约6.9万篇维基百科文章，横跨五个领域和八种语言。基于该数据集，我们训练了一个两阶段系统：输入为引用列表和章节标题，输出为针对特定章节的低资源语言摘要。该系统创新性地融合了神经无监督抽取式摘要（用于粗粒度识别关键信息）与神经抽象式摘要（用于生成章节专属文本）。大量实验表明，跨领域训练的平均表现优于多语言训练。