Lack of encyclopedic text contributors, especially on Wikipedia, makes automated text generation for low resource (LR) languages a critical problem. Existing work on Wikipedia text generation has focused on 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 XWikiGen, 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, XWikiRef, spanning ~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，使得低资源语言的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仅聚焦于英文领域，通过归纳英文参考文献生成英文维基页面。但对于低资源语言而言，参考文献的稀缺性导致单语摘要无法有效解决该问题。因此，本文提出跨语言多文档摘要任务XWikiGen，旨在整合来自多种语言的参考文献生成维基百科风格文本。我们为此构建了涵盖五个领域、八种语言约6.9万篇维基百科文章的基准数据集XWikiRef。利用该数据集，我们训练了一个两阶段系统：输入为引文集合与章节标题，输出为章节特定的低资源语言摘要。该系统基于神经无监督抽取式摘要的新颖思路，先粗粒度识别关键信息，再通过神经生成式模型生成章节特定文本。大量实验表明，跨领域训练的平均效果优于多语言设置。