Recently, compressive text summarisation offers a balance between the conciseness issue of extractive summarisation and the factual hallucination issue of abstractive summarisation. However, most existing compressive summarisation methods are supervised, relying on the expensive effort of creating a new training dataset with corresponding compressive summaries. In this paper, we propose an efficient and interpretable compressive summarisation method that utilises unsupervised dual-agent reinforcement learning to optimise a summary's semantic coverage and fluency by simulating human judgment on summarisation quality. Our model consists of an extractor agent and a compressor agent, and both agents have a multi-head attentional pointer-based structure. The extractor agent first chooses salient sentences from a document, and then the compressor agent compresses these extracted sentences by selecting salient words to form a summary without using reference summaries to compute the summary reward. To our best knowledge, this is the first work on unsupervised compressive summarisation. Experimental results on three widely used datasets (e.g., Newsroom, CNN/DM, and XSum) show that our model achieves promising performance and a significant improvement on Newsroom in terms of the ROUGE metric, as well as interpretability of semantic coverage of summarisation results.

翻译：近年来，压缩式文本摘要在抽取式摘要的简洁性问题与生成式摘要的事实幻觉问题之间取得了平衡。然而，现有的大多数压缩式摘要方法均采用监督学习范式，需要耗费大量资源创建带有对应压缩摘要的全新训练数据集。本文提出一种高效且可解释的压缩式摘要方法，该方法利用无监督双智能体强化学习，通过模拟人类对摘要质量的评判来优化摘要的语义覆盖度与流畅度。我们的模型包含抽取智能体和压缩智能体两个组件，两者均采用基于多头注意力指针网络的结构。抽取智能体首先从文档中选取关键句子，随后压缩智能体通过选择关键词汇对这些已抽取句子进行压缩以形成摘要，且无需借助参考摘要计算摘要奖励。据我们所知，这是首个关于无监督压缩式摘要的研究工作。在三个广泛使用的数据集（如Newsroom、CNN/DM和XSum）上的实验结果表明，我们的模型在ROUGE指标上取得了有竞争力的性能，特别是在Newsroom数据集上实现了显著提升，同时展现了摘要结果在语义覆盖度方面的可解释性。