Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization

Puyuan Liu; Chenyang Huang; Lili Mou

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arXiv · 2205.14521

Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization

Puyuan Liu·Chenyang Huang·Lili Mou

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Abstract

Text summarization aims to generate a short summary for an input text. In this work, we propose a Non-Autoregressive Unsupervised Summarization (NAUS) approach, which does not require parallel data for training. Our NAUS first performs edit-based search towards a heuristically defined score, and generates a summary as pseudo-groundtruth. Then, we train an encoder-only non-autoregressive Transformer based on the search result. We also propose a dynamic programming approach for length-control decoding, which is important for the summarization task. Experiments on two datasets show that NAUS achieves state-of-the-art performance for unsupervised summarization, yet largely improving inference efficiency. Further, our algorithm is able to perform explicit length-transfer summary generation.

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Puyuan Liu, Chenyang Huang, Lili Mou. 2022-05-28. Learning Non-Autoregressive Models from Search for Unsupervised Sentence Summarization. https://arxiv.org/abs/2205.14521

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