  Highlighting while reading is a natural behavior for people to track salient
content of a document. It would be desirable to teach an extractive summarizer
to do the same. However, a major obstacle to the development of a supervised
summarizer is the lack of ground-truth. Manual annotation of extraction units
is cost-prohibitive, whereas acquiring labels by automatically aligning human
abstracts and source documents can yield inferior results. In this paper we
describe a novel framework to guide a supervised, extractive summarization
system with question-answering rewards. We argue that quality summaries should
serve as a document surrogate to answer important questions, and such
question-answer pairs can be conveniently obtained from human abstracts. The
system learns to promote summaries that are informative, fluent, and perform
competitively on question-answering. Our results compare favorably with those
reported by strong summarization baselines as evaluated by automatic metrics
and human assessors.

Computation and Language