  We study the task of generating from Wikipedia articles question-answer pairs
that cover content beyond a single sentence. We propose a neural network
approach that incorporates coreference knowledge via a novel gating mechanism.
Compared to models that only take into account sentence-level information
(Heilman and Smith, 2010; Du et al., 2017; Zhou et al., 2017), we find that the
linguistic knowledge introduced by the coreference representation aids question
generation significantly, producing models that outperform the current
state-of-the-art. We apply our system (composed of an answer span extraction
system and the passage-level QG system) to the 10,000 top-ranking Wikipedia
articles and create a corpus of over one million question-answer pairs. We also
provide a qualitative analysis for this large-scale generated corpus from
Wikipedia.
