Unsupervised Neural Single-Document Summarization of Reviewsvia Learning Latent Discourse Structure and its Ranking
Abstract
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