  Recent approaches for unsupervised opinion summarization have predominantly
used the review reconstruction training paradigm. An encoder-decoder model is
trained to reconstruct single reviews and learns a latent review encoding
space. At summarization time, the unweighted average of latent review vectors
is decoded into a summary. In this paper, we challenge the convention of simply
averaging the latent vector set, and claim that this simplistic approach fails
to consider variations in the quality of input reviews or the idiosyncrasies of
the decoder. We propose Coop, a convex vector aggregation framework for opinion
summarization, that searches for better combinations of input reviews. Coop
requires no further supervision and uses a simple word overlap objective to
help the model generate summaries that are more consistent with input reviews.
Experimental results show that extending opinion summarizers with Coop results
in state-of-the-art performance, with ROUGE-1 improvements of 3.7% and 2.9% on
the Yelp and Amazon benchmark datasets, respectively.
