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Motivated by suggested question generation in conversational newsrecommendation systems, we propose a model for generating question-answer pairs(QA pairs) with self-contained, summary-centric questions andlength-constrained, article-summarizing answers. We begin by collecting a newdataset of news articles with questions as titles and pairing them withsummaries of varying length. This dataset is used to learn a QA pair generationmodel producing summaries as answers that balance brevity with sufficiencyjointly with their corresponding questions. We then reinforce the QA pairgeneration process with a differentiable reward function to mitigate exposurebias, a common problem in natural language generation. Both automatic metricsand human evaluation demonstrate these QA pairs successfully capture thecentral gists of the articles and achieve high answer accuracy.

Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning