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        <title>Affinity-Preserving Random Walk for Multi-Document Summarization</title>
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        <namePart type="given">Kexiang</namePart>
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    <abstract>Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.</abstract>
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%0 Conference Proceedings
%T Affinity-Preserving Random Walk for Multi-Document Summarization
%A Wang, Kexiang
%A Liu, Tianyu
%A Sui, Zhifang
%A Chang, Baobao
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F wang-etal-2017-affinity
%X Multi-document summarization provides users with a short text that summarizes the information in a set of related documents. This paper introduces affinity-preserving random walk to the summarization task, which preserves the affinity relations of sentences by an absorbing random walk model. Meanwhile, we put forward adjustable affinity-preserving random walk to enforce the diversity constraint of summarization in the random walk process. The ROUGE evaluations on DUC 2003 topic-focused summarization task and DUC 2004 generic summarization task show the good performance of our method, which has the best ROUGE-2 recall among the graph-based ranking methods.
%R 10.18653/v1/D17-1020
%U https://aclanthology.org/D17-1020
%U https://doi.org/10.18653/v1/D17-1020
%P 210-220

Markdown (Informal)

[Affinity-Preserving Random Walk for Multi-Document Summarization](https://aclanthology.org/D17-1020) (Wang et al., EMNLP 2017)

• Affinity-Preserving Random Walk for Multi-Document Summarization (Wang et al., EMNLP 2017)

ACL

• Kexiang Wang, Tianyu Liu, Zhifang Sui, and Baobao Chang. 2017. Affinity-Preserving Random Walk for Multi-Document Summarization. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 210–220, Copenhagen, Denmark. Association for Computational Linguistics.