  The availability of large-scale datasets has driven the development of neural
sequence-to-sequence models to generate generic summaries, i.e., summaries
which do not correspond to any pre-specified queries. However, due to the lack
of training data, query focused summarization (QFS) has been studied mainly
with extractive methods. In this work, we consider the problem of leveraging
only generic summarization resources to build an abstractive QFS system. We
propose Marge, a Masked ROUGE Regression framework composed of a novel unified
representation for summaries and queries, and a distantly supervised training
task for answer evidence estimation. To further utilize generic data for
generation, three attributes are incorporated during training and inference to
control the shape of the final summary: evidence rank, query guidance, and
summary length. Despite learning from minimal supervision, our system achieves
state-of-the-art results in the distantly supervised setting across domains and
query types.
