Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking

By Masaru Isonuma, Junichiro Mori, Ichiro Sakata. 2019

44 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

SummAE: Zero-Shot Abstractive Text Summarization using Length-Agnostic Auto-Encoders (2019, methodology): “Isonuma et al. (2019) proposed to summarize a product review by describing it as a discourse tree, where the summary is the root and the child sentences explain their parent.”
Few-Shot Learning for Opinion Summarization (2020, background): “Isonuma et al. (2019) introduce an unsupervised approach for single product review summarization, where they rely on latent discourse trees.”