  Query relevance ranking and sentence saliency ranking are the two main tasks
in extractive query-focused summarization. Previous supervised summarization
systems often perform the two tasks in isolation. However, since reference
summaries are the trade-off between relevance and saliency, using them as
supervision, neither of the two rankers could be trained well. This paper
proposes a novel summarization system called AttSum, which tackles the two
tasks jointly. It automatically learns distributed representations for
sentences as well as the document cluster. Meanwhile, it applies the attention
mechanism to simulate the attentive reading of human behavior when a query is
given. Extensive experiments are conducted on DUC query-focused summarization
benchmark datasets. Without using any hand-crafted features, AttSum achieves
competitive performance. It is also observed that the sentences recognized to
focus on the query indeed meet the query need.
