Hierarchical storyline generation method for hot news events

doi:10.11772/j.issn.1001-9081.2022091377

Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (8): 2376-2381.DOI: 10.11772/j.issn.1001-9081.2022091377

• The 19th International Conference on Web Information Systems and Applications (WISA 2022) • Previous Articles Next Articles

Hierarchical storyline generation method for hot news events

Dong LIU¹^,², Chuan LIN¹^,²(), Lina REN¹^,², Ruizhang HUANG¹^,²

^1.State Key Laboratory of Public Big Data （Guizhou University），Guiyang Guizhou 550025，China
^2.College of Computer Science and Technology，Guizhou University，Guiyang Guizhou 550025，China

Received:2022-09-06 Revised:2022-10-26 Accepted:2022-10-28 Online:2022-12-12 Published:2023-08-10
Contact: Chuan LIN
About author:LIU Dong， born in 1997， M. S. candidate. His research interests include natural language processing， text mining.
REN Lina， born in 1987， Ph. D. candidate， lecturer. Her research interests include natural language processing， text mining， machine learning.
HUANG Ruizhang， born in 1979， Ph. D.， professor. Her research interests include natural language understanding， data fusion analysis， text mining， knowledge discovery.
Supported by:
National Natural Science Foundation of China(62066007)

面向热点新闻事件的层次化故事脉络生成方法

刘东¹^,², 林川¹^,²(), 任丽娜¹^,², 黄瑞章¹^,²

^1.公共大数据国家重点实验室（贵州大学），贵阳 550025
^2.贵州大学计算机科学与技术学院，贵阳 550025

通讯作者: 林川
作者简介:刘东（1997—），男，四川成都人，硕士研究生，CCF会员，主要研究方向：自然语言处理、文本挖掘
任丽娜（1987—），女，辽宁阜新人，讲师，博士研究生，CCF会员，主要研究方向：自然语言处理、文本挖掘、机器学习
黄瑞章（1979—），女，天津人，教授，博士，主要研究方向：自然语言理解、数据融合分析、文本挖掘、知识发现。
基金资助:
国家自然科学基金资助项目(62066007)

Abstract

Abstract:

The development of hot news events is very rich， and each stage of the development has its own unique narrative. With the development of events， a trend of hierarchical storyline evolution is presented. Aiming at the problem of poor interpretability and insufficient hierarchy of storyline in the existing storyline generation methods， a Hierarchical Storyline Generation Method （HSGM） for hot news events was proposed. First， an improved hotword algorithm was used to select the main seed events to construct the trunk. Second， the hotwords of branch events were selected to enhance the branch interpretability. Third， in the branch， a storyline coherence selection strategy fusing hotword relevance and dynamic time penalty was used to enhance the connection of parent-child events， so as to build hierarchical hotwords， and then a multi-level storyline was built. In addition， considering the incubation period of hot news events， a hatchery was added during the storyline construction process to solve the problem of neglecting the initial events due to insufficient hotness. Experimental results on two real self-constructed datasets show that in the event tracking process， compared with the methods based on singlePass and k-means respectively， HSGM has the F score increased by 4.51% and 6.41%， 20.71% and 13.01% respectively； in the storyline construction process， HSGM performs well in accuracy， comprehensibility and integrity on two self-constructed datasets compared with Story Forest and Story Graph.

Key words: storyline, hot news event, story tree, event evolution, clustering

摘要：

热点新闻事件的发展十分丰富，各个阶段的发展都有其独特的叙述，并且随着事件的发展呈现出层次化故事脉络演化的趋势。针对现有故事脉络生成方法存在脉络可解释性不佳以及缺乏层次性的问题，提出一种面向热点新闻事件的层次化故事脉络生成方法（HSGM）。首先，采用改进热词算法来挑选主干种子事件，以构建主干脉络；其次，挑选分支事件热词以增强分支可解释性；然后，在分支脉络中采用融合热词关联度与动态时间惩罚的脉络连贯度挑选策略来增强父子事件的连接，以构建层次化热词，进而构建多层次故事脉络；此外，考虑到热点新闻事件存在潜伏期，在脉络构建过程加入孵化池以解决因热度不够所产生的初始事件被忽略问题。在两个自建真实数据集上进行实验的结果表明，在事件追踪过程中，与分别基于singlePass和基于k-means的方法相比，HSGM的F值分别高出了4.51%、6.41%和20.71%、13.01%；而在脉络构建过程中，与Story Forest和Story Graph相比，HSGM在两个自建数据集上的准确性、可理解性、完整性方面表现良好。

关键词: 故事脉络, 热点新闻事件, 故事树, 事件演化, 聚类

CLC Number:

TP391.1

Dong LIU, Chuan LIN, Lina REN, Ruizhang HUANG. Hierarchical storyline generation method for hot news events[J]. Journal of Computer Applications, 2023, 43(8): 2376-2381.

刘东, 林川, 任丽娜, 黄瑞章. 面向热点新闻事件的层次化故事脉络生成方法[J]. 《计算机应用》唯一官方网站, 2023, 43(8): 2376-2381.

Figures/Tables 7

References 25

1	张仰森，段宇翔，黄改娟，等. 社交媒体话题检测与追踪技术研究综述［J］. 中文信息学报， 2019， 33（7）： 1-10. 10.3969/j.issn.1003-0077.2019.07.001
	ZHANG Y S， DUAN Y X， HUANG G J， et al. A survey on topic detection and tracking methods in social media［J］. Journal of Chinese Information Processing， 2019， 33（7）： 1-10. 10.3969/j.issn.1003-0077.2019.07.001
2	XU G X， MENG Y T， CHEN Z， et al. Research on topic detection and tracking for online news texts［J］. IEEE Access， 2019， 7： 58407-58418. 10.1109/access.2019.2914097
3	张晨昕，饶元，樊笑冰，等. 基于社交媒体的事件脉络挖掘研究进展［J］. 中文信息学报， 2019， 33（11）： 15-30. 10.3969/j.issn.1003-0077.2019.11.002
	ZHANG C X， RAO Y， FAN X B， et al. Research progress of event summarization based on social media［J］. Journal of Chinese Information Processing， 2019， 33（11）： 15-30. 10.3969/j.issn.1003-0077.2019.11.002
4	赵旭剑，王崇伟，金培权，等. 面向Web的故事脉络挖掘研究综述［J］. 中文信息学报， 2021， 35（11）： 13-33. 10.3969/j.issn.1003-0077.2021.11.002
	ZHAO X J， WANG C W， JIN P Q， et al. A survey of Web-oriented storyline mining［J］. Journal of Chinese Information Processing， 2021， 35（11）： 13-33. 10.3969/j.issn.1003-0077.2021.11.002
5	付佳兵，董守斌. 一种基于词覆盖的新闻事件脉络链构建方法［J］. 北京大学学报（自然科学版）， 2016， 52（1）： 104-112.
	FU J B， DONG S B. Constructing a news story chain from word coverage perspective［J］. Acta Scientiarum Naturalium Universitatis Pekinensis， 2016， 52（1）： 104-112.
6	LIU B， NIU D， LAI K F， et al. Growing story forest online from massive breaking news［C］// Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York： ACM， 2017： 777-785. 10.1145/3132847.3132852
7	LIU B， HAN F X， NIU D， et al. Story forest： extracting events and telling stories from breaking news［J］. ACM Transactions on Knowledge Discovery from Data， 2020， 14（3）： No.31. 10.1145/3377939
8	ANSAH J， LIU L， KANG W， et al. A graph is worth a thousand words： telling event stories using timeline summarization graphs［C］// Proceedings of the 2019 World Wide Web Conference. Republic and Canton of Geneva： International World Wide Web Conferences Steering Committee， 2019： 2565-2571. 10.1145/3308558.3313396
9	GOYAL P， KAUSHIK P， GUPTA P， et al. Multilevel event detection， storyline generation， and summarization for tweet streams［J］. IEEE Transactions on Computational Social Systems， 2020， 7（1）： 8-23. 10.1109/tcss.2019.2954116
10	ZHOU D Y， GUO L S， HE Y L. Neural storyline extraction model for storyline generation from news articles［C］// Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics： Human Language Technologies， Volume 1 （Long Papers）. Stroudsburg， PA： ACL， 2018： 1727-1736. 10.18653/v1/n18-1156
11	佘玉轩，熊赟. 基于贝叶斯网络的故事线挖掘算法［J］. 计算机工程， 2018， 44（3）： 55-59. 10.3969/j.issn.1000-3428.2018.03.009
	SHE Y X， XIONG Y. Storyline mining algorithm based on Bayesian network［J］. Computer Engineering， 2018： 44（3）： 55-59. 10.3969/j.issn.1000-3428.2018.03.009
12	GUO L S， ZHOU D Y， HE Y L， et al. Storyline extraction from news articles with dynamic dependency［J］. Intelligent Data Analysis， 2020， 24（1）： 183-197. 10.3233/ida-184448
13	赵天资，段亮，岳昆，等. 基于Biterm主题模型的新闻线索生成方法［J］. 数据分析与知识发现， 2021， 5（2）： 1-13.
	ZHAO T Z， DUAN L， YUE K， et al. Generating news clues with Biterm topic model［J］. Data Analysis and Knowledge Discovery， 2021， 5（2）： 1-13.
14	李莹莹，马帅，蒋浩谊，等. 一种基于社交事件关联的故事脉络生成方法［J］. 计算机研究与发展， 2018， 55（9）： 1972-1986. 10.7544/issn1000-1239.2018.20180155
	LI Y Y， MA S， JIANG H Y， et al. An approach for storytelling by correlating events from social networks［J］. Journal of Computer Research and Development， 2018， 55（9）： 1972-1986. 10.7544/issn1000-1239.2018.20180155
15	DEHGHANI N， ASADPOUR M. SGSG： semantic graph-based storyline generation in twitter［J］. Journal of Information Science， 2019， 45（3）： 304-321. 10.1177/0165551518775304
16	SUN W J， WANG Y H， GAO Y Q， et al. Comprehensive event storyline generation from microblogs［C］// Proceedings of the 2019 ACM Multimedia Asia. New York： ACM， 2019： No.48. 10.1145/3338533.3366601
17	樊笑冰，饶元，王硕，等. 基于命名实体敏感的分层新闻故事线生成方法［J］. 中文信息学报， 2021， 35（1）： 113-124.
	FAN X B， RAO Y， WANG S， et al. Named entity sensitive generation of hierarchical news storyline［J］. Journal of Chinese Information Processing， 2021， 35（1）： 113-124.
18	陈黎明，黄瑞章，秦永彬，等. 面向新闻事件的故事树构建方法［J］. 计算机工程与设计， 2020， 41（7）： 1910-1919.
	CHEN L M， HUANG R Z， QIN Y B， et al. Story tree construction approach for news events［J］. Computer Engineering and Design， 2020， 41（7）： 1910-1919.
19	丁梦佩. 社交网络中的话题漂移研究［D］. 北京：北京邮电大学， 2019：1-68.
	DING M P. Research on topic drift in social networks［D］. Beijing： Beijing University of Posts and Telecommunications， 2019：1-68.
20	王小林，杨林，王东，等. 改进的TF-IDF 关键词提取方法［J］. 计算机科学与应用， 2013， 3（1）： 64-68.
	WANG X L， YANG L， WANG D， et al. Improved TF-IDF keyword extraction algorithm［J］. Computer Science and Application， 2013， 3（1）： 64-68.
21	贵州耕云科技有限公司. 一种基于标题高频切分的新闻热点短语提取方法： 201710743020.1［P］. 2018-01-09. 10.26789/jwc.2020.01.002
	Guizhou Cloud Pioneer Technology Co. Ltd. A news hot word phrase extraction method based on title high frequency segmentation： 201710743020.1［P］. 2018-01-09. 10.26789/jwc.2020.01.002
22	LI S， ZHAO Z， HU R F， et al. Analogical reasoning on Chinese morphological and semantic relations［C］// Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics （Volume 2： Short Papers）. Stroudsburg， PA： ACL， 2018： 138-143. 10.18653/v1/p18-2023
23	ROUSSEEUW P J. Silhouettes： a graphical aid to the interpretation and validation of cluster analysis［J］. Journal of Computational and Applied Mathematics， 1987， 20： 53-65. 10.1016/0377-0427(87)90125-7
24	PAPKA R， ALLAN J. On-line new event detection using single pass clustering［C］// Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval. New York： ACM， 1998： 37-45. 10.1145/290941.290954
25	MacQUEEN J. Some methods for classification and analysis of multivariate observations［C］// Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability， Volume 1： Statistics. Oakland， CA： University of California Press， 1967： 281-297.

数据集	相关文档数	无关文档数	时间
Dataset1	536	1 000	2021-04至2021-06
Dataset2	320	600	2022-02至2022-04

数据集	相关文档数	无关文档数	时间
Dataset1	536	1 000	2021-04至2021-06
Dataset2	320	600	2022-02至2022-04

数据集	HSGM			基于singlePass的方法			基于k-means的方法
数据集	准确率	召回率	F值	准确率	召回率	F值	准确率	召回率	F值
Dataset1	97.06	98.15	97.51	97.96	97.10	93.30	92.53	94.14	91.64
Dataset2	90.69	73.61	82.95	87.41	56.95	68.72	85.11	61.10	73.40

数据集	HSGM			基于singlePass的方法			基于k-means的方法
数据集	准确率	召回率	F值	准确率	召回率	F值	准确率	召回率	F值
Dataset1	97.06	98.15	97.51	97.96	97.10	93.30	92.53	94.14	91.64
Dataset2	90.69	73.61	82.95	87.41	56.95	68.72	85.11	61.10	73.40

指标	数据集	HSGM	Story Forest	Story Graph
准确性	Dataset1	3.94	3.94	3.83
准确性	Dataset2	3.83	3.77	3.72
可理解性	Dataset1	4.33	4.17	4.06
可理解性	Dataset2	4.28	4.06	4.00
完整性	Dataset1	4.22	4.11	3.94
完整性	Dataset2	4.17	4.06	3.94

Hierarchical storyline generation method for hot news events

面向热点新闻事件的层次化故事脉络生成方法

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