Page 250 - 《软件学报》2025年第5期

2150 软件学报 2025 年第 36 卷第 5 期

[67] Yasunaga M, Zhang R, Meelu K, Pareek A, Srinivasan K, Radev D. Graph-based neural multi-document summarization. In: Proc. of the
21st Conf. on Computational Natural Language Learning. Vancouver: Association for Computational Linguistics, 2017. 452–462. [doi:
10.18653/v1/K17-1045]
[68] Nguyen DQ, Vu T, Nguyen AT. BERTweet: A pre-trained language model for English Tweets. In: Proc. of the 2020 Conf. on Empirical
Methods in Natural Language Processing: System Demonstrations. Online: Association for Computational Linguistics, 2020. 9–14. [doi:
10.18653/v1/2020.emnlp-demos.2]
[69] Lin CY. ROUGE: A package for automatic evaluation of summaries. In: Text Summarization Branches Out. Barcelona: Association for
Computational Linguistics, 2004. 74–81.
[70] Radev DR, Blair-Goldensohn S, Zhang Z. Experiments in single and multi-document summarization using MEAD. In: Proc. of the 1st
Document Understanding Conf. 2001. 1194–1197.
[71] Gong YH, Liu X. Generic text summarization using relevance measure and latent semantic analysis. In: Proc. of the 24th Annual Int’l
ACM SIGIR Conf. on Research and Development in Information Retrieval. New Orleans: ACM, 2001. 19–25. [doi: 10.1145/383952.
383955]
[72] Erkan G, Radev DR. LexRank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence
Research, 2004, 22(1): 457–479.
[73] He ZY, Chen C, Bu JJ, Wang C, Zhang LJ, Cai D, He XF. Document summarization based on data reconstruction. In: Proc. of the 26th
AAAI Conf. on Artificial Intelligence. Toronto: AAAI Press, 2012. 620–626.
[74] Liu H, Yu HL, Deng ZH. Multi-document summarization based on two-level sparse representation model. In: Proc. of the 29th AAAI
Conf. on Artificial Intelligence. Austin: AAAI Press, 2015. 196–202.
[75] Zheng H, Lapata M. Sentence centrality revisited for unsupervised summarization. In: Proc. of the 57th Annual Meeting of the
Association for Computational Linguistics. Florence: Association for Computational Linguistics, 2019. 6236–6247. [doi: 10.18653/v1/
P19-1628]
[76] Wang KX, Chang BB, Sui ZF. A spectral method for unsupervised multi-document summarization. In: Proc. of the 2020 Conf. on
Empirical Methods in Natural Language Processing. Online: Association for Computational Linguistics, 2020. 435–445. [doi: 10.18653/
v1/2020.emnlp-main.32]

附中文参考文献:
[49] 韩毅, 许进, 方滨兴, 周斌, 贾焰. 社交网络的结构支撑理论. 计算机学报, 2014, 37(4): 905–914. [doi: 10.3724/SP.J.1016.2014.00905]
[50] 曹玖新, 吴江林, 石伟, 刘波, 郑啸, 罗军舟. 新浪微博网信息传播分析与预测. 计算机学报, 2014, 37(4): 779–790. [doi: 10.3724/SP.J.
1016.2014.00779]

贺瑞芳(1979－), 女, 博士, 教授, CCF 专业会员, 刘焕宇(1997－), 男, 硕士, 主要研究领域为自然
主要研究领域为自然语言处理, 社会媒体挖掘, 语言处理, 文本摘要.
机器学习.

赵堂龙(2000－), 男, 硕士生, 主要研究领域为自
然语言处理, 文本摘要.