Query based event extraction along a timeline

ABSTRACT

In this paper, we present a framework and a system that extracts events relevant to a query from a collection C of documents, and places such events along a timeline. Each event is represented by a sentence extracted from C, based on the assumption that "important" events are widely cited in many documents for a period of time within which these events are of interest. In our experiments, we used queries that are event types ("earthquake") and person names (e.g. "George Bush"). Evaluation was performed using G8 leader names as queries: comparison made by human evaluators between manually and system generated timelines showed that although manually generated timelines are on average more preferable, system generated timelines are sometimes judged to be better than manually constructed ones.

REFERENCES

• 1: James Allan , Rahul Gupta , Vikas Khandelwal, Temporal summaries of new topics, Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval, p.10-18, September 2001, New Orleans, Louisiana, United States [doi>10.1145/383952.383954]
• 2: Regina Barzilay , Kathleen R. McKeown , Michael Elhadad, Information fusion in the context of multi-document summarization, Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics, p.550-557, June 20-26, 1999, College Park, Maryland [doi>10.3115/1034678.1034760]
• 3: Regina Barzilay, Noemie Elhadad, and Kathleen McKeown. Inferring Strategies for Sentence Ordering in Multidocument News Summarization. Journal of Artificial Intelligence Research 17: 35--55, 2002.
• 4: Regina Barzilay , Kathleen R. Mckeown, Information fusion for multidocument summarization: paraphrasing and generation, 2003
• 5: Ted Dunning, Accurate methods for the statistics of surprise and coincidence, Computational Linguistics, v.19 n.1, March 1993
• 6: Lisa Ferro, Inderjeet Mani, Beth Sundheim and George Wilson. TIDES Temporal Annotation Guidelines Version 1.0.2. Mitre Technical Report. 2001.
• 7: Vasileios Hatzivassiloglou, Judith Klavans, Melissa Holcombe, Regina Barzilay, Min-Yen Kan, and Kathleen KcKeown. SimFinder: A Flexible Clustering tool for Summarization. In Proceedings of the Workshop on Automatic Summarization, NAACL2001, pages 41--49, 2001.
• 8: Jon Kleinberg, Bursty and hierarchical structure in streams, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775061]
• 9: Kevin Knight , Daniel Marcu, Statistics-Based Summarization - Step One: Sentence Compression, Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence, p.703-710, July 30-August 03, 2000
• 10: Julian Kupiec , Jan Pedersen , Francine Chen, A trainable document summarizer, Proceedings of the 18th annual international ACM SIGIR conference on Research and development in information retrieval, p.68-73, July 09-13, 1995, Seattle, Washington, United States [doi>10.1145/215206.215333]
• 11: Inderjeet Mani , Eric Bloedorn, Summarizing Similarities and Differences Among Related Documents, Information Retrieval, v.1 n.1-2, p.35-67, 1999 [doi>10.1023/A:1009930203452]
• 12: Inderjeet Mani , George Wilson, Robust temporal processing of news, Proceedings of the 38th Annual Meeting on Association for Computational Linguistics, p.69-76, October 03-06, 2000, Hong Kong [doi>10.3115/1075218.1075228]
• 13: Kathleen R. McKeown , Judith L. Klavans , Vasileios Hatzivassiloglou , Regina Barzilay , Eleazar Eskin, Towards multidocument summarization by reformulation: progress and prospects, Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence, p.453-460, July 18-22, 1999, Orlando, Florida, United States
• 14: Kathleen McKeown, Vasileios Hatzivassiloglou, Regina Barzilay, Barry Schiffman, David Evans and Simone Teufel. Columbia multi-document summarization: approach and evaluation. In Proceedings of the Document Understanding Workshop (DUC), 2001.
• 15: Dragomir R. Radev , Kathleen R. McKeown, Generating natural language summaries from multiple on-line sources, Computational Linguistics, v.24 n.3, September 1998
• 16: Barry Schiffman , Inderjeet Mani , Kristian J. Concepcion, Producing biographical summaries: combining linguistic knowledge with corpus statistics, Proceedings of the 39th Annual Meeting on Association for Computational Linguistics, p.458-465, July 06-11, 2001, Toulouse, France [doi>10.3115/1073012.1073071]
• 17: David A. Smith, Detecting and Browsing Events in Unstructured text, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, August 11-15, 2002, Tampere, Finland [doi>10.1145/564376.564391]
• 18: Russell Swan , James Allan, Extracting significant time varying features from text, Proceedings of the eighth international conference on Information and knowledge management, p.38-45, November 02-06, 1999, Kansas City, Missouri, United States [doi>10.1145/319950.319956]
• 19: Russell Swan , James Allan, Automatic generation of overview timelines, Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval, p.49-56, July 24-28, 2000, Athens, Greece [doi>10.1145/345508.345546]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.1 Content Analysis and Indexing
          Subjects: Linguistic processing

Additional Classification:
  H. Information Systems
  H.3 INFORMATION STORAGE AND RETRIEVAL
      H.3.3 Information Search and Retrieval
          Subjects: Selection process

General Terms:
Algorithms, Experimentation

Keywords:
automatic summarization, event and entity tracking, text data mining, timelines