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首页> 外文会议>Annual meeting of the Association for Computational Linguistics >Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking
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Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking

机译：通过学习潜入话语结构及其排名，无监督的神经单一文件摘要评论

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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.
机译：本文重点介绍了未经监督的单一产品审查的端到端抽象总结。我们假设可以将审查描述为话语树，其中摘要是根，儿童句子详细解释他们的父母。通过递归估计其子名的父母，我们的模型在没有外部解析器的情况下学习潜在话语树并生成简明摘要。我们还介绍了一种架构，该架构将树上的每个句子的重要性排名，以支持摘要一代关注主要审查点。实验结果表明，我们的模型与其他无监督的方法竞争或优于其他无监督的方法。特别是，对于相对漫长的评论，它实现了比监督模型的竞争性或更好的性能。诱导树显示儿童句子提供有关其父级的其他信息，而生成的摘要摘要整个审查。

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