  This paper focuses on the end-to-end abstractive summarization of a single
product review without supervision. We assume that a review can be described as
a discourse tree, in which the summary is the root, and the child sentences
explain their parent in detail. By recursively estimating a parent from its
children, our model learns the latent discourse tree without an external parser
and generates a concise summary. We also introduce an architecture that ranks
the importance of each sentence on the tree to support summary generation
focusing on the main review point. The experimental results demonstrate that
our model is competitive with or outperforms other unsupervised approaches. In
particular, for relatively long reviews, it achieves a competitive or better
performance than supervised models. The induced tree shows that the child
sentences provide additional information about their parent, and the generated
summary abstracts the entire review.
