  We investigate a new training paradigm for extractive summarization.
Traditionally, human abstracts are used to derive goldstandard labels for
extraction units. However, the labels are often inaccurate, because human
abstracts and source documents cannot be easily aligned at the word level. In
this paper we convert human abstracts to a set of Cloze-style comprehension
questions. System summaries are encouraged to preserve salient source content
useful for answering questions and share common words with the abstracts. We
use reinforcement learning to explore the space of possible extractive
summaries and introduce a question-focused reward function to promote concise,
fluent, and informative summaries. Our experiments show that the proposed
method is effective. It surpasses state-of-the-art systems on the standard
summarization dataset.
