Extractive summarization aims to form a summary by directly extracting sentences from the source document. Existing works mostly formulate it as a sequence labeling problem by making individual sentence label predictions. This paper proposes DiffuSum, a novel paradigm for extractive summarization, by directly generating the desired summary sentence representations with diffusion models and extracting sentences based on sentence representation matching. In addition, DiffuSum jointly optimizes a contrastive sentence encoder with a matching loss for sentence representation alignment and a multi-class contrastive loss for representation diversity. Experimental results show that DiffuSum achieves the new state-of-the-art extractive results on CNN/DailyMail with ROUGE scores of $44.83/22.56/40.56$. Experiments on the other two datasets with different summary lengths also demonstrate the effectiveness of DiffuSum. The strong performance of our framework shows the great potential of adapting generative models for extractive summarization. To encourage more following work in the future, we have released our codes at \url{https://github.com/hpzhang94/DiffuSum}

翻译：抽取式摘要旨在通过直接提取源文档中的句子来构成摘要。现有研究大多将其表述为序列标注问题，对每个句子进行独立标签预测。本文提出DiffuSum，一种用于抽取式摘要的新范式，通过扩散模型直接生成目标摘要句子表示，并基于句子表示匹配进行句子抽取。此外，DiffuSum联合优化对比句子编码器，通过匹配损失实现句子表示对齐，并通过多类对比损失实现表示多样性。实验结果表明，DiffuSum在CNN/DailyMail数据集上取得了最新的抽取式摘要最优结果，ROUGE分数分别为$44.83/22.56/40.56$。在另外两个不同摘要长度的数据集上的实验也证明了DiffuSum的有效性。我们框架的强劲性能展示了将生成模型适配到抽取式摘要的巨大潜力。为鼓励未来更多相关研究，我们已在\url{https://github.com/hpzhang94/DiffuSum}上公开代码。