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.

翻译：摘要：抽取式摘要旨在通过直接从源文档中提取句子来形成摘要。现有研究大多将其建模为序列标注问题，即对每个句子进行独立标签预测。本文提出一种全新的抽取式摘要范式DiffuSum，该方法直接利用扩散模型生成期望的摘要句子表征，并通过句子表征匹配来提取句子。此外，DiffuSum联合优化了一个对比性句子编码器：采用匹配损失实现句子表征对齐，并引入多类对比损失保证表征多样性。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佳结果，ROUGE得分为44.83/22.56/40.56。在另外两个具有不同摘要长度的数据集上的实验也验证了DiffuSum的有效性。本框架的优异性能表明，将生成式模型应用于抽取式摘要任务具有巨大潜力。