  The recent success of deep learning techniques for abstractive summarization
is predicated on the availability of large-scale datasets. When summarizing
reviews (e.g., for products or movies), such training data is neither available
nor can be easily sourced, motivating the development of methods which rely on
synthetic datasets for supervised training. We show that explicitly
incorporating content planning in a summarization model not only yields output
of higher quality, but also allows the creation of synthetic datasets which are
more natural, resembling real world document-summary pairs. Our content plans
take the form of aspect and sentiment distributions which we induce from data
without access to expensive annotations. Synthetic datasets are created by
sampling pseudo-reviews from a Dirichlet distribution parametrized by our
content planner, while our model generates summaries based on input reviews and
induced content plans. Experimental results on three domains show that our
approach outperforms competitive models in generating informative, coherent,
and fluent summaries that capture opinion consensus.
