  We carry out experiments with deep learning models of summarization across
the domains of news, personal stories, meetings, and medical articles in order
to understand how content selection is performed. We find that many
sophisticated features of state of the art extractive summarizers do not
improve performance over simpler models. These results suggest that it is
easier to create a summarizer for a new domain than previous work suggests and
bring into question the benefit of deep learning models for summarization for
those domains that do have massive datasets (i.e., news). At the same time,
they suggest important questions for new research in summarization; namely, new
forms of sentence representations or external knowledge sources are needed that
are better suited to the summarization task.
