Article

Temporal summaries of new topics

Authors:

Vikas KhandelwalAuthors Info & Claims

SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

Pages 10 - 18

https://doi.org/10.1145/383952.383954

Published: 01 September 2001 Publication History

Abstract

We discuss technology to help a person monitor changes in news coverage over time. We define temporal summaries of news stories as extracting a single sentence from each event within a news topic, where the stories are presented one at a time and sentences from a story must be ranked before the next story can be considered. We explain a method for evaluation, and describe an evaluation corpus that we have built. We also propose several methods for constructing temporal summaries and evaluate their effectiveness in comparison to degenerate cases. We show that simple approaches are effective, but that the problem is far from solved.

References

[1]

J. Allan, J. Carbonell, G. Doddington, J. Yamron, and Y. Yang. Topic detection and tracking pilot study: Final report. In Proceedings of the DARPA Broadcast News Transcription and Understanding Workshop, pages 194-218, 1998.

[2]

J. Allan, H. Jin, M. Rajman, C. Wayne, D. Gildea, V. L. vrenko, R. Hoberman, and D. Caputo. Topic-based novelty detection: 1999 summer workshop at CLSP, Final report. Available at http://www.clsp.jhu.edu/ws99/tdt, 1999.

[3]

C. Aone, M. Okurowski, J. Gorlinsky, and B. Larsen. A trainable summarizer with knowledge acquired from robust nlp techniques. In Mani and Maybury {21}.

[4]

A. Berker and V. Mittal. OCELOT: a system for summarizing web pages. In Proceedings of SIGIR, pages 144--151, 2000.

Digital Library

[5]

R. Brandow, K. Mitze, and L. Rau. Automatic condensation of electronic publications by sentence selection. In Mani and Maybury {21}. Originally published in Information Processing and Management.

Digital Library

[6]

J. Carbonell and J. Goldstein. The use of MMR, diversity-based reranking for reordering documents and producting summaries. In Proceedings of SIGIR, pages 335-336, 1998.

Digital Library

[7]

DARPA, editor. Proceedings of the DARPA Broadcast news Workshop, Herndon, Virginia, Feb. 1999.

[8]

DARPA, editor. The TDT-2 Text and Speech Corpus, Herndon, Virginia, Feb. 1999.

[9]

R. Donaway, K. Drummey, and L. Mather. A comparison of rankings produced by summarization evaluation measures. In Hahn et al. {15}, pages 69-78.

[10]

H. Edmundson. New methods in automatic extracting. In Mani and Maybury {21}. Originally published in Journal of the ACM.

Digital Library

[11]

T. Firmin and M. Chrzanowski. An evaluation of automatic text summarization systems. In Mani and Maybury {21}.

[12]

F. Fukumoto and Y. Suzuki. Extracting key paragraph based on topic and event detection. In Hahn et al. {15}, pages 31-39.

[13]

J. Goldstein, M. Kantrowitz, V. Mittal, and J. Carbonell. Summarizing text documents: Sentence selection and evaluation metrics. In Proceedings of SIGIR, pages 121-128, 1999.

Digital Library

[14]

J. Goldstein, V. Mittal, J. Carbonell, and M. Kantrowitz. Multi-document summarization by sentence extraction. In Hahn et al. {15}, pages 40-48.

[15]

U. Hahn, C. Lin, I. Mani, and D. Radev, editors. Automatic Summarization: ANLP/NAACL 2000 Workshop,New Brunswick, NJ, 2000. Association for Computational Linguistics.

[16]

E. Hovy and C. Lin. Automated text summarization in SUMMARIST. In Mani and Maybury {21}.

[17]

V. Khandelwal, R. Gupta, and J. Allan. An evaluation scheme for summarizing topic shifts in news streams, 2001. Submitted to Human Language Technology Conference.

[18]

J. Kupiec, J. Pedersen, and F. Chen. A trainable document summarizer. In Mani and Maybury {21}. Originally published in Proceedings of SIGIR.

Digital Library

[19]

H. Luhn. The automatic creation of literature abstracts. In Mani and Maybury {21}. Originally published in IBM Journal of R&D.

Digital Library

[20]

I. Mani and E. Bloedorn. Summarizing similarities and differences among related documents. In Mani and Maybury {21}. Originally published in Information Retrieval.

Digital Library

[21]

I. Mani and M. Maybury, editors. Advances in Automatic Text Summarization. MIT Press, Cambridge, Massachusetts, 1999.

Digital Library

[22]

I. Mani and G. Wilson. Robust temporal processing of news. In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL 2000), pages 69-76, New Brunswick, New Jersey, 2000. Association for Computational Linguistics.

Digital Library

[23]

C. Manning and H. Schutze. Foundations of Statistical Natural Language Processing. MIT Press, Cambridge, Massachusetts, 1999.

Digital Library

[24]

M. Maybury. Generating summaries from event data. In Mani and Maybury {21}. Originally published in Information Processing and Management.

Digital Library

[25]

K. McKeown and D. Radev. Generating summaries of multiple news articles. In Mani and Maybury {21}.

[26]

A. Morris, G. Kasper, and D. Adams. The effects and limitations of automated text condensing on reading comprehension preformance. In Mani and Maybury {21}. Originally published in Information Systems Research.

[27]

S. Myaeng and D. Jang. Development and evaluation of a statistically based document summarization system. In Mani and Maybury {21}.

[28]

NIST. Proceedings of the tdt 1999 workshop. Notebook publication for participants only, Mar. 2000.

[29]

NIST. Proceedings of the tdt 2000 workshop. Notebook publication for participants only, Nov. 2000.

[30]

M. Oka and Y. Ueda. Evaluation of phrase-representation summarization based on information retrieval task. In Hahn et al. {15}, pages 59-68.

[31]

M. Okurowski, H. Wilson, J. Urbina, R. Clark, T. Taylor, and F. Krapcho. Text summarizer in use: Lessons learned from real world deployment and evaluation. In Hahn et al. {15}, pages 49-58.

[32]

J. Pollock and A. Zamora. Automatic abstracting research at chemical abstracts service. In Mani and Maybury {21}. Originally published in Journal of Chemical Information and Computer Science.

[33]

J. Ponte and W. Croft. A language modeling approach to information retrieval. In Proceedings of SIGIR, pages 275-281, 1998.

Digital Library

[34]

D. Radev, H. Jing, and M. Budzikowska. Centroid-based summarization of multiple documents: sentence extraction, utility-based evaluation, and user studies. In Hahn et al. {15}, pages 21-30.

[35]

G. Rath, A. Resnick, and T. Savage. The formation of abstracts by the selection of sentences. In Mani and Maybury {21}. Originally published in American Documentation (now JASIS).

[36]

H. Saggion and G. Lapalme. Concept identification and presentation in the context of technical text summarization. In Hahn et al. {15}, pages 1-10.

[37]

G. Salton, A. Singhal, M. Mitra, and C. Buckley. Automatic text structuring and summarization. In Mani and Maybury {21}. Originally published in Information Processing and Management.

Digital Library

[38]

R. Swan and J. Allan. Automatic generation of overview timelines. In Proceedings of SIGIR, pages 49-56, Athens, Greece, 2000. ACM.

Digital Library

[39]

M. Witbrock and V. Mittal. Ulta-summarization : A statistical approach to generating highly condensed non-extractive summaries. In Proceedings of SIGIR, pages 315-316, 1999. Poster description.

Digital Library

Cited By

Jarvelin KSormunen E(2024)A Blueprint of IR Evaluation Integrating Task and User CharacteristicsACM Transactions on Information Systems10.1145/367516242:6(1-38)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3675162
Chen XGao SLi MZhu QGao XZhang X(2024)Write Summary Step-by-Step: A Pilot Study of Stepwise SummarizationIEEE/ACM Transactions on Audio, Speech, and Language Processing10.1109/TASLP.2024.335704032(1406-1415)Online publication date: 2024
https://doi.org/10.1109/TASLP.2024.3357040
You JLi DKamigaito HFunakoshi KOkumura M(2023)Joint Learning-based Heterogeneous Graph Attention Network for Timeline SummarizationJournal of Natural Language Processing10.5715/jnlp.30.18430:1(184-214)Online publication date: 2023
https://doi.org/10.5715/jnlp.30.184
Show More Cited By

Index Terms

Temporal summaries of new topics
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing
    2. Retrieval models and ranking
2. Theory of computation
  1. Logic
    1. Modal and temporal logics

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Published In

cover image ACM Conferences

SIGIR '01: Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval

September 2001

454 pages

ISBN:1581133316

DOI:10.1145/383952

Chairmen:
Donald H. Kraft
Louisiana State Univ.
,
W. Bruce Croft
University of Massachusetts, (For the Americas)
,
David J. Harper
The Robert Gordon University, (For Europe and Africa)
,
Justin Zobel
RMIT University, (For Asia and Australasia)

Copyright © 2001 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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SIGIR: ACM Special Interest Group on Information Retrieval

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Association for Computing Machinery

New York, NY, United States

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Published: 01 September 2001

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SIGIR

SIGIR01: 24th ACM/SIGIR International Conference on Research and Development in Information Retrieval

Louisiana, New Orleans, USA

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SIGIR '01 Paper Acceptance Rate 47 of 201 submissions, 23%;

Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Jarvelin KSormunen E(2024)A Blueprint of IR Evaluation Integrating Task and User CharacteristicsACM Transactions on Information Systems10.1145/367516242:6(1-38)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3675162
Chen XGao SLi MZhu QGao XZhang X(2024)Write Summary Step-by-Step: A Pilot Study of Stepwise SummarizationIEEE/ACM Transactions on Audio, Speech, and Language Processing10.1109/TASLP.2024.335704032(1406-1415)Online publication date: 2024
https://doi.org/10.1109/TASLP.2024.3357040
You JLi DKamigaito HFunakoshi KOkumura M(2023)Joint Learning-based Heterogeneous Graph Attention Network for Timeline SummarizationJournal of Natural Language Processing10.5715/jnlp.30.18430:1(184-214)Online publication date: 2023
https://doi.org/10.5715/jnlp.30.184
Liu MZhang MLiu JDai HYang MJi SFeng ZGong BOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Video timeline modeling for news story understandingProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667352(28294-28310)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667352
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
Chen XLi MGao SChan ZZhao DGao XZhang XYan R(2023)Follow the Timeline! Generating an Abstractive and Extractive Timeline Summary in Chronological OrderACM Transactions on Information Systems10.1145/351722141:1(1-30)Online publication date: 9-Jan-2023
https://dl.acm.org/doi/10.1145/3517221
Carichon FFettu FCaporossi G(2023)Unsupervised update summarization of news eventsPattern Recognition10.1016/j.patcog.2023.109839144(109839)Online publication date: Dec-2023
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Yang MQu QShen YZhao ZChen XLi C(2021)An Effective Hybrid Learning Model for Real-Time Event SummarizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.301774732:10(4419-4431)Online publication date: Oct-2021
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Piñeros-Enciso JGalpin I(2021)Analysing Documents About Colombian Indigenous Peoples Through Text MiningApplied Informatics10.1007/978-3-030-89654-6_10(125-140)Online publication date: 23-Oct-2021
https://doi.org/10.1007/978-3-030-89654-6_10
Pasquali ACampos RRibeiro ASantana BJorge AJatowt A(2021)TLS-Covid19: A New Annotated Corpus for Timeline SummarizationAdvances in Information Retrieval10.1007/978-3-030-72113-8_33(497-512)Online publication date: 27-Mar-2021
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