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.

这篇论文提出了一个新的提取性摘要范式，名为 DiffuSum，通过扩散模型直接生成所需的摘要句子表示，并根据句子表示匹配来提取句子，并通过多类对比损失对表示多样性进行优化，实验结果表明 DiffuSum 在 CNN / DailyMail 上实现了新的最先进的提取结果，ROUGE 得分为 $44.83 / 22.56 / 40.56$，并且在其他两个数据集上的实验结果也表明了 DiffuSum 的有效性。