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cs.CL2021
Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning
Li Zhou, Kevin Small, Yong Zhang +1
Motivated by suggested question generation in conversational news recommendation systems, we propose a model for generating question-answer pairs (QA pairs) with self-contained, su…
cs.CL2021
Evaluating the Tradeoff Between Abstractiveness and Factuality in Abstractive Summarization
Markus Dreyer, Mengwen Liu, Feng Nan +2
Neural models for abstractive summarization tend to generate output that is fluent and well-formed but lacks semantic faithfulness, or factuality, with respect to the input documen…