The Summary Loop: Learning to Write Abstractive Summaries Without Examples

• URL: http://arxiv.org/abs/2105.05361v1
• Date: Tue, 11 May 2021 23:19:46 GMT
• Title: The Summary Loop: Learning to Write Abstractive Summaries Without Examples
• Authors: Philippe Laban, Andrew Hsi, John Canny, Marti A. Hearst
• Abstract summary: This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. Key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary. When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points.
• Score: 21.85348918324668
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This work presents a new approach to unsupervised abstractive summarization based on maximizing a combination of coverage and fluency for a given length constraint. It introduces a novel method that encourages the inclusion of key terms from the original document into the summary: key terms are masked out of the original document and must be filled in by a coverage model using the current generated summary. A novel unsupervised training procedure leverages this coverage model along with a fluency model to generate and score summaries. When tested on popular news summarization datasets, the method outperforms previous unsupervised methods by more than 2 R-1 points, and approaches results of competitive supervised methods. Our model attains higher levels of abstraction with copied passages roughly two times shorter than prior work, and learns to compress and merge sentences without supervision.

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