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        <title>DiffuSum: Generation Enhanced Extractive Summarization with Diffusion</title>
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        <namePart type="given">Haopeng</namePart>
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    <abstract>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 and cross-dataset evaluation also demonstrate the effectiveness of DiffuSum. The strong performance of our framework shows the great potential of adapting generative models for extractive summarization.</abstract>
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    <identifier type="doi">10.18653/v1/2023.findings-acl.828</identifier>
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%0 Conference Proceedings
%T DiffuSum: Generation Enhanced Extractive Summarization with Diffusion
%A Zhang, Haopeng
%A Liu, Xiao
%A Zhang, Jiawei
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F zhang-etal-2023-diffusum
%X 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 and cross-dataset evaluation also demonstrate the effectiveness of DiffuSum. The strong performance of our framework shows the great potential of adapting generative models for extractive summarization.
%R 10.18653/v1/2023.findings-acl.828
%U https://aclanthology.org/2023.findings-acl.828/
%U https://doi.org/10.18653/v1/2023.findings-acl.828
%P 13089-13100

Markdown (Informal)

[DiffuSum: Generation Enhanced Extractive Summarization with Diffusion](https://aclanthology.org/2023.findings-acl.828/) (Zhang et al., Findings 2023)

• DiffuSum: Generation Enhanced Extractive Summarization with Diffusion (Zhang et al., Findings 2023)

ACL

• Haopeng Zhang, Xiao Liu, and Jiawei Zhang. 2023. DiffuSum: Generation Enhanced Extractive Summarization with Diffusion. In Findings of the Association for Computational Linguistics: ACL 2023, pages 13089–13100, Toronto, Canada. Association for Computational Linguistics.