  Opinion summarization is the task of automatically generating summaries for a
set of reviews about a specific target (e.g., a movie or a product). Since the
number of reviews for each target can be prohibitively large, neural
network-based methods follow a two-stage approach where an extractive step
first pre-selects a subset of salient opinions and an abstractive step creates
the summary while conditioning on the extracted subset. However, the extractive
model leads to loss of information which may be useful depending on user needs.
In this paper we propose a summarization framework that eliminates the need to
rely only on pre-selected content and waste possibly useful information,
especially when customizing summaries. The framework enables the use of all
input reviews by first condensing them into multiple dense vectors which serve
as input to an abstractive model. We showcase an effective instantiation of our
framework which produces more informative summaries and also allows to take
user preferences into account using our zero-shot customization technique.
Experimental results demonstrate that our model improves the state of the art
on the Rotten Tomatoes dataset and generates customized summaries effectively.
