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arXiv · 1908.08566

Unsupervised Text Summarization via Mixed Model Back-Translation

Yacine Jernite
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Abstract

Back-translation based approaches have recently lead to significant progress in unsupervised sequence-to-sequence tasks such as machine translation or style transfer. In this work, we extend the paradigm to the problem of learning a sentence summarization system from unaligned data. We present several initial models which rely on the asymmetrical nature of the task to perform the first back-translation step, and demonstrate the value of combining the data created by these diverse initialization methods. Our system outperforms the current state-of-the-art for unsupervised sentence summarization from fully unaligned data by over 2 ROUGE, and matches the performance of recent semi-supervised approaches.

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Yacine Jernite. 2019-08-22. Unsupervised Text Summarization via Mixed Model Back-Translation. https://arxiv.org/abs/1908.08566

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