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SeburSum: a novel set-based summary ranking strategy for summary-level extractive summarization

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Abstract

Summary-level extractive summarization is often regarded as a text-matching task, which selects the summary that is semantically closest to the source document by a matching model. However, the method tends to select candidate summaries with more sentences, because it calculates the semantic similarity between the candidate summaries and the source document. To address the issue, we propose a novel set-based summary ranking strategy for extractive summarization called SeburSum, which selects a summary by examining its semantic similarity with mutually exclusive candidate summaries, rather than its similarity to the source document. In contrast to conventional extractive summarization methods that rely on well-trained extractive models trained on labeled data to select sentences, the ranking strategy eliminates the need for labeled data and enhances the versatility of both supervised and unsupervised extractive summarization. To improve the calculation accuracy of semantic similarity between candidates, we construct a contrastive learning framework with a task-specific contrastive loss to learn vector representations for each candidate summary. Experimental results show that in the supervised extractive summarization, we achieve state-of-the-art extractive performance on the CNN/Daily Mail, Reddit, and Xsum with ROUGE-1 scores of 45.49, 26.71, and 25.77, respectively, and outperform the previous summary-level baseline by 1.08, 1.62, and 0.91. In the unsupervised extractive summarization, we achieved state-of-the-art performance on the CNN/Daily Mail dataset with 42.97, 20.14 and 39.14 for ROUGE-1, ROUGE-2 and ROUGE-L, respectively, outperforming the latest state-of-the-art results by 1.71, 1.96, and 1.93.

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Availability of data and materials

The data and code that support the findings of this study are openly available at https://github.com/GongShuai8210/SeburSum.

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Acknowledgements

The work in this paper is supported by the National Social Science Foundation of China (19BYY076) and Shandong Natural Science Foundation (ZR2021MF064, ZR2021QG041). We would like to thank our teachers for their careful guidance. We also thank the members of our NLP group for their helpful discussions. We sincerely thank the volunteers for their evaluation of the summaries. Finally, we would like to thank all authors for their contributions and anonymous reviewers for their constructive comments.

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  1. School of Artificial Intelligence, Shandong Jiaotong University, Haitang, Jinan, 250399, Shandong, China

    Shuai Gong, Zhenfang Zhu, Jiangtao Qi, Wenqing Wu & Chunling Tong

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  2. Zhenfang Zhu
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  3. Jiangtao Qi
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Contributions

We list author contributions as follows. SG conceptualization, methodology, software, writing—original draft and formal analysis. ZZ validation, writing,review and editing, resources, funding acquisition, project administration. JQ investigation, writing—review and editing, project administration. WW validation, investigation, writing—review and editing. CT validation, investigation. All authors reviewed the manuscript

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Gong, S., Zhu, Z., Qi, J. et al. SeburSum: a novel set-based summary ranking strategy for summary-level extractive summarization. J Supercomput 79, 12949–12977 (2023). https://doi.org/10.1007/s11227-023-05165-8

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