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.

翻译：句子摘要在保持文本核心内容的同时缩短了给定文本, 研究了未经监督的文本摘要方法, 没有人文摘要。 但是, 最近的未经监督的模式是采掘的, 这些模式从文本中删除了文字, 因而不那么灵活于抽象的总结。 在这项工作中, 我们设计了一个基于强化学习的抽象模式, 没有地面真相摘要。 我们根据Markov 决策程序制定了未经监督的总结方法, 其奖赏代表了摘要质量。 为了进一步提高摘要质量, 我们开发了一个多摘要学习机制, 生成多个摘要, 给某一文本的长度不同, 使摘要相互加强。 实验结果显示, 拟议的模型在实质上超越了抽象和采掘模式, 但是经常生成输入文本中未包含的新词。