We address the problem of unsupervised abstractive summarization of collections of user generated reviews through self-supervision and control. We propose a self-supervised setup that considers an individual document as a target summary for a set of similar documents. This setting makes training simpler than previous approaches by relying only on standard log-likelihood loss and mainstream models. We address the problem of hallucinations through the use of control codes, to steer the generation towards more coherent and relevant summaries. Our benchmarks on two English datasets against graph-based and recent neural abstractive unsupervised models show that our proposed method generates summaries with a superior quality and relevance, as well as a high sentiment and topic alignment with the input reviews. This is confirmed in our human evaluation which focuses explicitly on the faithfulness of generated summaries. We also provide an ablation study showing the importance of the control setup in controlling hallucinations.
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