research-article

A Graph is Worth a Thousand Words: Telling Event Stories using Timeline Summarization Graphs

Authors:

Selasie Kwashie,

Jiuyong LiAuthors Info & Claims

WWW '19: The World Wide Web Conference

Pages 2565 - 2571

https://doi.org/10.1145/3308558.3313396

Published: 13 May 2019 Publication History

Abstract

Story timeline summarization is widely used by analysts, law enforcement agencies, and policymakers for content presentation, story-telling, and other data-driven decision-making applications. Recent advancements in web technologies have rendered social media sites such as Twitter and Facebook as a viable platform for discovering evolving stories and trending events for story timeline summarization. However, a timeline summarization structure that models complex evolving stories by tracking event evolution to identify different themes of a story and generate a coherent structure that is easy for users to understand is yet to be explored. In this paper, we propose StoryGraph, a novel graph timeline summarization structure that is capable of identifying the different themes of a story. By using high penalty metrics that leverage user network communities, temporal proximity, and the semantic context of the events, we construct coherent paths and generate structural timeline summaries to tell the story of how events evolve over time. We performed experiments on real-world datasets to show the prowess of StoryGraph. StoryGraph outperforms existing models and produces accurate timeline summarizations. As a key finding, we discover that user network communities increase coherence leading to the generation of consistent summary structures.

References

[1]

Hamed Abdelhaq, Christian Sengstock, and Michael Gertz. 2013. EvenTweet: Online Localized Event Detection from Twitter. PVLDB 6, 12 (2013), 1326-1329.

Digital Library

[2]

Dhekar Abhik and Durga Toshniwal. 2013. Sub-event detection during natural hazards using features of social media data. In Proceedings of the 22nd International Conference on World Wide Web. ACM, 783-788.

Digital Library

[3]

Animashree Anandkumar, Rong Ge, Daniel Hsu, Sham M Kakade, and Matus Telgarsky. 2014. Tensor decompositions for learning latent variable models. The Journal of Machine Learning Research 15, 1 (2014), 2773-2832.

Digital Library

[4]

Jeffery Ansah, Wei Kang, Lin Liu, Jixue Liu, and Jiuyong Li. 2018. Information Propagation Trees for Protest Event Prediction. In PAKDD 2018. Springer, 777-789.

Digital Library

[5]

Jeffery Ansah, Wei Kang, Lin Liu, Jixue Liu, and Jiuyong Li. 2018. SensorTree: Bursty Propagation Trees as Sensors for Protest Event Detection. In Web Information Systems Engineering - WISE 2018 - 19th International Conference, Dubai, United Arab Emirates, November 12-15, 2018, Proceedings, Part I. 281-296.

[6]

David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993-1022.

Digital Library

[7]

Jose Cadena, Gizem Korkmaz, Chris J Kuhlman, Achla Marathe, Naren Ramakrishnan, and Anil Vullikanti. 2015. Forecasting social unrest using activity cascades. PloS one 10, 6 (2015), e0128879.

[8]

Deepayan Chakrabarti and Kunal Punera. 2011. Event Summarization Using Tweets.ICWSM 11(2011), 66-73.

[9]

Yi Chang, Jiliang Tang, Dawei Yin, Makoto Yamada, and Yan Liu. 2016. Timeline Summarization from Social Media with Life Cycle Models. In IJCAI. 3698-3704.

Digital Library

[10]

Ruifang He, Yang Liu, Guangchuan Yu, Jiliang Tang, Qinghua Hu, and Jianwu Dang. 2017. Twitter summarization with social-temporal context. World Wide Web 20, 2 (2017), 267-290.

Digital Library

[11]

Karen Spärck Jones. 2007. Automatic summarising: The state of the art. Information Processing & Management 43, 6 (2007), 1449-1481.

Digital Library

[12]

April Kontostathis, Leon M Galitsky, William M Pottenger, Soma Roy, and Daniel J Phelps. 2004. A survey of emerging trend detection in textual data mining. In Survey of text mining. Springer, 185-224.

[13]

Solomon Kullback and Richard A Leibler. 1951. On information and sufficiency. The annals of mathematical statistics 22, 1 (1951), 79-86.

[14]

Chen Lin, Chun Lin, Jingxuan Li, Dingding Wang, Yang Chen, and Tao Li. 2012. Generating event storylines from microblogs. In Proceedings of the 21st ACM international conference on Information and knowledge management. ACM, 175-184.

Digital Library

[15]

Chin-Yew Lin and Eduard H. Hovy. 2003. Automatic Evaluation of Summaries Using N-gram Co-occurrence Statistics. In HLT-NAACL.

Digital Library

[16]

Bang Liu, Di Niu, Kunfeng Lai, Linglong Kong, and Yu Xu. 2017. Growing Story Forest Online from Massive Breaking News. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management. ACM, 777-785.

Digital Library

[17]

Quanchao Liu, Heyan Huang, and Chong Feng. 2013. Micro-blog post topic drift detection based on LDA model. In Behavior and Social Computing. Springer, 106-118.

Digital Library

[18]

Deepa Mallela, Dirk Ahlers, and Maria Soledad Pera. 2017. Mining Twitter features for event summarization and rating. In Proceedings of the International Conference on Web Intelligence. ACM, 615-622.

Digital Library

[19]

Christopher D Manning, Christopher D Manning, and Hinrich Schütze. 1999. Foundations of statistical natural language processing. MIT press.

Digital Library

[20]

Adam Marcus, Michael S Bernstein, Osama Badar, David R Karger, Samuel Madden, and Robert C Miller. 2011. Twitinfo: aggregating and visualizing microblogs for event exploration. In Proceedings of the SIGCHI conference on Human factors in computing systems. ACM, 227-236.

Digital Library

[21]

Ramesh Nallapati, Ao Feng, Fuchun Peng, and James Allan. 2004. Event threading within news topics. In Proceedings of the thirteenth ACM international conference on Information and knowledge management. ACM, 446-453.

Digital Library

[22]

Ani Nenkova and Kathleen McKeown. 2012. A survey of text summarization techniques. In Mining text data. Springer, 43-76.

[23]

Jeffrey Nichols, Jalal Mahmud, and Clemens Drews. 2012. Summarizing sporting events using twitter. In Proceedings of the 2012 ACM international conference on Intelligent User Interfaces. ACM, 189-198.

Digital Library

[24]

Kevin Dela Rosa, Rushin Shah, Bo Lin, Anatole Gershman, and Robert Frederking. 2011. Topical clustering of tweets. Proceedings of the ACM SIGIR: SWSM(2011).

[25]

Dafna Shahaf and Carlos Guestrin. 2010. Connecting the dots between news articles. In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 623-632.

Digital Library

[26]

Imane Tamimi, El Khadir Lamrani, and Mohamed El Kamili. 2017. Community Detection Through Topic Modeling in Social Networks. In International Symposium on Ubiquitous Networking. Springer, 70-80.

[27]

Vikas Thada and Vivek Jaglan. 2013. Comparison of jaccard, dice, cosine similarity coefficient to find best fitness value for web retrieved documents using genetic algorithm. International Journal of Innovations in Engineering and Technology 2, 4(2013), 202-205.

[28]

Han Wang and Jia-Ling Koh. 2017. Timeline Summarization for Event-Related Discussions on a Chinese Social Media Platform. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems. Springer, 579-594.

[29]

Shize Xu, Shanshan Wang, and Yan Zhang. 2013. Summarizing complex events: a cross-modal solution of storylines extraction and reconstruction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. 1281-1291.

[30]

Christopher C Yang, Xiaodong Shi, and Chih-Ping Wei. 2009. Discovering event evolution graphs from news corpora. IEEE Transactions on Systems, Man, and Cybernetics-Part A: Systems and Humans 39, 4 (2009), 850-863.

Digital Library

Cited By

Gui FYang JTang YChen HAn N(2024)Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature RefinementApplied Sciences10.3390/app1402091814:2(918)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020918
Garg PChakraborty RDandapat S(2024)ADSumm: annotated ground-truth summary datasets for disaster tweet summarizationSocial Network Analysis and Mining10.1007/s13278-024-01323-914:1Online publication date: 5-Aug-2024
https://doi.org/10.1007/s13278-024-01323-9
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cover image ACM Other conferences

WWW '19: The World Wide Web Conference

May 2019

3620 pages

ISBN:9781450366748

DOI:10.1145/3308558

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19

WWW '19: The Web Conference

May 13 - 17, 2019

CA, San Francisco, USA

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Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

Gui FYang JTang YChen HAn N(2024)Structured Life Narratives: Building Life Story Hierarchies with Graph-Enhanced Event Feature RefinementApplied Sciences10.3390/app1402091814:2(918)Online publication date: 22-Jan-2024
https://doi.org/10.3390/app14020918
Garg PChakraborty RDandapat S(2024)ADSumm: annotated ground-truth summary datasets for disaster tweet summarizationSocial Network Analysis and Mining10.1007/s13278-024-01323-914:1Online publication date: 5-Aug-2024
https://doi.org/10.1007/s13278-024-01323-9
Muller MSaaman Evan der Werf JJeurgens CReijers H(2024)TimeFlows: Visualizing Process Chronologies from Vast Collections of Heterogeneous Information ObjectsResearch Challenges in Information Science10.1007/978-3-031-59465-6_13(203-219)Online publication date: 2-May-2024
https://doi.org/10.1007/978-3-031-59465-6_13
Wang YSun GZhu ZLi TChen LLiang R(2023)EStoryline: Visualizing the Relationship with Triplet Entities and Event DiscoveryACM Transactions on Intelligent Systems and Technology10.1145/363351915:1(1-26)Online publication date: 23-Nov-2023
https://dl.acm.org/doi/10.1145/3633519
Jones SKlein MWeigle MNelson M(2023)Summarizing Web Archive Corpora via Social Media Storytelling by Automatically Selecting and Visualizing ExemplarsACM Transactions on the Web10.1145/360603018:1(1-48)Online publication date: 11-Oct-2023
https://dl.acm.org/doi/10.1145/3606030
Keith Norambuena BMitra TNorth C(2023)A Survey on Event-Based News Narrative ExtractionACM Computing Surveys10.1145/358474155:14s(1-39)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1145/3584741
Dusart APinel-Sauvagnat KHubert G(2023)TSSuBERT: How to Sum Up Multiple Years of Reading in a Few TweetsACM Transactions on Information Systems10.1145/358178641:4(1-33)Online publication date: 10-Apr-2023
https://dl.acm.org/doi/10.1145/3581786
Keith Norambuena BMitra TNorth C(2023)Mixed Multi-Model Semantic Interaction for Graph-based Narrative VisualizationsProceedings of the 28th International Conference on Intelligent User Interfaces10.1145/3581641.3584076(866-888)Online publication date: 27-Mar-2023
https://dl.acm.org/doi/10.1145/3581641.3584076
Yoon SChan HHan J(2023)PDSum: Prototype-driven Continuous Summarization of Evolving Multi-document Sets StreamProceedings of the ACM Web Conference 202310.1145/3543507.3583371(1650-1661)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583371
Yoon SLee DZhang YHan JChen HDuh WHuang HKato MMothe JPoblete B(2023)Unsupervised Story Discovery from Continuous News Streams via Scalable Thematic EmbeddingProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591782(802-811)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591782
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