基于无监督双智能体强化学习的高效可解释压缩式文本摘要
Efficient and Interpretable Compressive Text Summarisation with Unsupervised Dual-Agent Reinforcement Learning
AI总结：
提出首个无监督压缩式文本摘要方法，通过extractor与compressor双智能体强化学习优化语义覆盖和流畅性，无需参考摘要，并在Newsroom、CNN/DM和XSum上验证有效。
摘要：
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性幻觉问题之间提供了一种平衡。然而，现有的大多数压缩式摘要方法都是监督式的，依赖于高昂的成本来创建带有相应压缩摘要的新训练数据集。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判断来优化摘要的语义覆盖度和流畅性。我们的模型由一个extractor智能体和一个compressor智能体组成，两个智能体均采用基于多头注意力的pointer结构。extractor智能体首先从文档中选择显著句子，随后compressor智能体通过选择显著词语来压缩这些被抽取出的句子以形成摘要，并且无需使用参考摘要来计算摘要奖励。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，我们的模型取得了可观的性能，在Newsroom上的ROUGE指标上获得显著提升，并在摘要结果的语义覆盖度方面具有可解释性。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
