Sentence summarization shortens given texts while maintaining core contents
of the texts. Unsupervised approaches have been studied to summarize texts
without human-written summaries. However, recent unsupervised models are
extractive, which remove words from texts and thus they are less flexible than
abstractive summarization. In this work, we devise an abstractive model based
on reinforcement learning without ground-truth summaries. We formulate the
unsupervised summarization based on the Markov decision process with rewards
representing the summary quality. To further enhance the summary quality, we
develop a multi-summary learning mechanism that generates multiple summaries
with varying lengths for a given text, while making the summaries mutually
enhance each other. Experimental results show that the proposed model
substantially outperforms both abstractive and extractive models, yet
frequently generating new words not contained in input texts.

本篇论文提出了一种基于强化学习的抽象模型，用于无人工摘要的句子摘要，同时还开发了一种多摘要机制来提高摘要质量。实验结果表明，该模型明显优于抽象和提取式模型，经常生成未包含在原始文本中的新单词。