  Generating engaging content has drawn much recent attention in the NLP
community. Asking questions is a natural way to respond to photos and promote
awareness. However, most answers to questions in traditional question-answering
(QA) datasets are factoids, which reduce individuals' willingness to answer.
Furthermore, traditional visual question generation (VQG) confines the source
data for question generation to single images, resulting in a limited ability
to comprehend time-series information of the underlying event. In this paper,
we propose generating engaging questions from multiple images. We present MVQG,
a new dataset, and establish a series of baselines, including both end-to-end
and dual-stage architectures. Results show that building stories behind the
image sequence enables models to generate engaging questions, which confirms
our assumption that people typically construct a picture of the event in their
minds before asking questions. These results open up an exciting challenge for
visual-and-language models to implicitly construct a story behind a series of
photos to allow for creativity and experience sharing and hence draw attention
to downstream applications.
