  Multi-document question generation focuses on generating a question that
covers the common aspect of multiple documents. Such a model is useful in
generating clarifying options. However, a naive model trained only using the
targeted ("positive") document set may generate too generic questions that
cover a larger scope than delineated by the document set. To address this
challenge, we introduce the contrastive learning strategy where given
"positive" and "negative" sets of documents, we generate a question that is
closely related to the "positive" set but is far away from the "negative" set.
This setting allows generated questions to be more specific and related to the
target document set. To generate such specific questions, we propose
Multi-Source Coordinated Question Generator (MSCQG), a novel framework that
includes a supervised learning (SL) stage and a reinforcement learning (RL)
stage. In the SL stage, a single-document question generator is trained. In the
RL stage, a coordinator model is trained to find optimal attention weights to
align multiple single-document generators, by optimizing a reward designed to
promote specificity of generated questions. We also develop an effective
auxiliary objective, named Set-induced Contrastive Regularization (SCR) that
improves the coordinator's contrastive learning during the RL stage. We show
that our model significantly outperforms several strong baselines, as measured
by automatic metrics and human evaluation. The source repository is publicly
available at \url{www.github.com/woonsangcho/contrast_qgen}.
