emnlp emnlp2013 emnlp2013-147 emnlp2013-147-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Lifu Huang ; Lian'en Huang
Abstract: Recently, much research focuses on event storyline generation, which aims to produce a concise, global and temporal event summary from a collection of articles. Generally, each event contains multiple sub-events and the storyline should be composed by the component summaries of all the sub-events. However, different sub-events have different part-whole relationship with the major event, which is important to correspond to users’ interests but seldom considered in previous work. To distinguish different types of sub-events, we propose a mixture-event-aspect model which models different sub-events into local and global aspects. Combining these local/global aspects with summarization requirements together, we utilize an optimization method to generate the component summaries along the timeline. We develop experimental systems on 6 distinctively different datasets. Evaluation and comparison results indicate the effectiveness of our proposed method.
David M Blei, Andrew Y Ng, and Michael IJordan. 2003. Latent dirichlet allocation. the Journal of machine Learning research, 3:993–1022. Jackie Chi Kit Cheung, Giuseppe Carenini, and Raymond T Ng. 2009. Optimization-based content selection for opinion summarization. In Proceedings of the 2009 Workshop on Language Generation and Summarisation, pages 7–14. ACL. Hai Leong Chieu and Yoong Keok Lee. 2004. Query based event extraction along a timeline. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 425–432. ACM. G. Erkan and D.R. Radev. 2004. Lexpagerank: Prestige in multi-document text summarization. In Proceedings of EMNLP, volume 4. Thomas Hofmann. 1999. Probabilistic latent semantic analysis. In Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pages 289– 296. Morgan Kaufmann Publishers Inc. Giridhar Kumaran and James Allan. 2004. Text classification and named entities for new event detection. In Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval, pages 297–304. ACM. Theodoros Lappas, Benjamin Arai, Manolis Platakis, Dimitrios Kotsakos, and Dimitrios Gunopulos. 2009. On burstiness-aware search for document sequences. In Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 477–486. ACM. Peng Li, Jing Jiang, and Yinglin Wang. 2010. Generating templates of entity summaries with an entityaspect model and pattern mining. In Proceedings of the 48th annual meeting of the Association for Computational Linguistics, pages 640–649. ACL. C.Y. Lin and E. Hovy. 2003. Automatic evaluation of summaries using n-gram co-occurrence statistics. In Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language TechnologyVolume 1, pages 71–78. ACL. 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, pages 175–184. ACM. Juha Makkonen, Helena Ahonen-Myka, and Marko Salmenkivi. 2004. Simple semantics in topic detection and tracking. Information Retrieval, 7(3-4):347– 368. Qiaozhu Mei and ChengXiang Zhai. 2005. Discovering evolutionary theme patterns from text: an exploration of temporal text mining. In Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining, pages 198–207. ACM. Q. Mei, J. Guo, and D. Radev. 2010. Divrank: the interplay of prestige and diversity in information networks. 735 In Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1009–1018. ACM. Thomas Minka. 2000. Estimating a dirichlet distribution. Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The pagerank citation ranking: bringing order to the web. Yulong Pei, Wenpeng Yin, et al. 2012. Generic multidocument summarization using topic-oriented information. In PRICAI 2012: Trends in Artificial Intelligence, pages 435–446. Springer. D.R. Radev, H. Jing, M. Sty s´, and D. Tam. 2004. Centroid-based summarization of multiple documents. Information Processing & Management, 40(6):919– 938. Mauricio GC Resende and Renato F Werneck. 2004. A hybrid heuristic for the p-median problem. Journal of heuristics, 10(1):59–88. Ivan Titov and Ryan McDonald. 2008. Modeling online reviews with multi-grain topic models. In Pro- ceedings of the 17th international conference on World Wide Web, pages 111–120. ACM. X. Wan. 2008. Document-based hits model for multidocument summarization. PRICAI 2008: Trends in Artificial Intelligence, pages 454–465. Xuanhui Wang, ChengXiang Zhai, Xiao Hu, and Richard Sproat. 2007. Mining correlated bursty topic patterns from coordinated text streams. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 784– 793. ACM. Xiang Wang, Kai Zhang, Xiaoming Jin, and Dou Shen. 2009. Mining common topics from multiple asynchronous text streams. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, pages 192–201 . ACM. Dingding Wang, Tao Li, and Mitsunori Ogihara. 2012. Generating pictorial storylines via minimum-weight connected dominating set approximation in multi-view graphs. Proceddings of AAAI 2012. Rui Yan, Liang Kong, Congrui Huang, Xiaojun Wan, Xiaoming Li, and Yan Zhang. 2011a. Timeline generation through evolutionary trans-temporal summarization. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 433– 443. ACL. Rui Yan, Xiaojun Wan, Jahna Otterbacher, Liang Kong, Xiaoming Li, and Yan Zhang. 2011b. Evolutionary timeline summarization: a balanced optimization framework via iterative substitution. In Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pages 745–754. ACM.