  Recent advances in text autoencoders have significantly improved the quality
of the latent space, which enables models to generate grammatical and
consistent text from aggregated latent vectors. As a successful application of
this property, unsupervised opinion summarization models generate a summary by
decoding the aggregated latent vectors of inputs. More specifically, they
perform the aggregation via simple average. However, little is known about how
the vector aggregation step affects the generation quality. In this study, we
revisit the commonly used simple average approach by examining the latent space
and generated summaries. We found that text autoencoders tend to generate
overly generic summaries from simply averaged latent vectors due to an
unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we
refer to as summary vector degeneration. To overcome this issue, we develop a
framework Coop, which searches input combinations for the latent vector
aggregation using input-output word overlap. Experimental results show that
Coop successfully alleviates the summary vector degeneration issue and
establishes new state-of-the-art performance on two opinion summarization
benchmarks. Code is available at \url{https://github.com/megagonlabs/coop}.
