Unsupervised Text Summarization via Mixed Model Back Translation
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.
ArXiv