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Text summarization aims to generate a short summary for an input text. Inthis work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS)approach, which does not require parallel data for training. Our NAUS firstperforms edit-based search towards a heuristically defined score, and generatesa summary as pseudo-groundtruth. Then, we train an encoder-onlynon-autoregressive Transformer based on the search result. We also propose adynamic programming approach for length-control decoding, which is importantfor the summarization task. Experiments on two datasets show that NAUS achievesstate-of-the-art performance for unsupervised summarization, yet largelyimproving inference efficiency. Further, our algorithm is able to performexplicit length-transfer summary generation.

Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization