  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}
