  Emerged as one of the best performing techniques for extractive
summarization, determinantal point processes select the most probable set of
sentences to form a summary according to a probability measure defined by
modeling sentence prominence and pairwise repulsion. Traditionally, these
aspects are modelled using shallow and linguistically informed features, but
the rise of deep contextualized representations raises an interesting question
of whether, and to what extent, contextualized representations can be used to
improve DPP modeling. Our findings suggest that, despite the success of deep
representations, it remains necessary to combine them with surface indicators
for effective identification of summary sentences.
