  Abstractive summarization has been studied using neural sequence transduction
methods with datasets of large, paired document-summary examples. However, such
datasets are rare and the models trained from them do not generalize to other
domains. Recently, some progress has been made in learning sequence-to-sequence
mappings with only unpaired examples. In our work, we consider the setting
where there are only documents (product or business reviews) with no summaries
provided, and propose an end-to-end, neural model architecture to perform
unsupervised abstractive summarization. Our proposed model consists of an
auto-encoder where the mean of the representations of the input reviews decodes
to a reasonable summary-review while not relying on any review-specific
features. We consider variants of the proposed architecture and perform an
ablation study to show the importance of specific components. We show through
automated metrics and human evaluation that the generated summaries are highly
abstractive, fluent, relevant, and representative of the average sentiment of
the input reviews. Finally, we collect a reference evaluation dataset and show
that our model outperforms a strong extractive baseline.
