  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.
