How can we generate concise explanations for multi-hop Reading Comprehension
(RC)? The current strategies of identifying supporting sentences can be seen as
an extractive question-focused summarization of the input text. However, these
extractive explanations are not necessarily concise i.e. not minimally
sufficient for answering a question. Instead, we advocate for an abstractive
approach, where we propose to generate a question-focused, abstractive summary
of input paragraphs and then feed it to an RC system. Given a limited amount of
human-annotated abstractive explanations, we train the abstractive explainer in
a semi-supervised manner, where we start from the supervised model and then
train it further through trial and error maximizing a conciseness-promoted
reward function. Our experiments demonstrate that the proposed abstractive
explainer can generate more compact explanations than an extractive explainer
with limited supervision (only 2k instances) while maintaining sufficiency.

提出了一种基于抽象化的方法来生成问题相关的简明解释，该方法通过半监督的学习方式训练模型，使用一种关注简洁性的奖励函数来获得更为紧凑的解释。实验证明，在有限的监督下，提出的抽象化解释器生成的解释比传统的摘要解释器更为简洁。