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Estimation of the Representative Story Transition in a Chronological Semantic Structure of News Topics

Authors:

Kosuke Kato,

Ichiro Ide,

Daisuke Deguchi,

Hiroshi MuraseAuthors Info & Claims

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

Pages 487 - 490

https://doi.org/10.1145/2578726.2578800

Published: 01 April 2014 Publication History

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Abstract

It is important to track the flow of topics to thoroughly understand the contents. Accordingly, a method that structures the chronological semantic relations between news stories, namely a "topic thread structure" has been proposed. It allows the comprehensive understanding of a topic by chronologically tracking stories one by one from the initial story. However, this task imposes a user to watch many stories when it contains various sub-topics. Thus, we propose a method that estimates the representative story transition in a topic thread structure. In the proposed method, features obtained from a story and those from the topic thread structure are used for the estimation. We confirmed the effectiveness of the proposed method by comparing the results obtained from the proposed method to the ground truth obtained from votes in a subjective experiment.

References

[1]

P. Duygulu, J. Pan, and D.A. Forsyth, "Towards auto-documentary: Tracking the evolution of news stories", Proc. 12th ACM Int. Conf. Multimedia, pp.820--827, Oct. 2004.

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[2]

X. Wu, C. Ngo, and Q. Li, "Threading and autodocumenting news videos", IEEE Signal Processing Mag., Vol.23, No.2, pp.59--68, Mar. 2006.

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[3]

I. Ide, T. Kinoshita, T. Takahashi, H. Mo, N. Katayama, S. Satoh, and H. Murase, "Efficient tracking of news topics based on chronological semantic structures in a large-scale news video archive", IEICE Trans. Information & Systems, Vol.E95-D, No.5, pp.1288--1300, May 2012.

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[4]

R. Sawai, H. Senoo, and Y. Shishikui, "Proposal and evaluation of a method for calculating news value for creating news digest (In Japanese)", IPSJ Trans. Databases, Vol.2, No.2, pp.158--172, Jun. 2009.

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Cited By

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Meena PKumar HKumar Yadav S(2023)A review on video summarization techniquesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105667118:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105667
Gupta DSharma A(2023)A comprehensive study of automatic video summarization techniquesArtificial Intelligence Review10.1007/s10462-023-10429-z56:10(11473-11633)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10462-023-10429-z
Tiwari VBhatnagar C(2021)A survey of recent work on video summarization: approaches and techniquesMultimedia Tools and Applications10.1007/s11042-021-10977-y80:18(27187-27221)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s11042-021-10977-y
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Index Terms

Estimation of the Representative Story Transition in a Chronological Semantic Structure of News Topics
1. Information systems
  1. Information retrieval
    1. Document representation

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Published In

ICMR '14: Proceedings of International Conference on Multimedia Retrieval

April 2014

564 pages

ISBN:9781450327824

DOI:10.1145/2578726

Conference Chairs:
Mohan Kankanhalli
National University of Singapore
,
Stefan Rueger
The Open University, UK
,
R. Manmatha
A9.com, USA
,
General Chairs:
Joemon Jose
University of Glasgow, UK
,
Keith van Rijsbergen
University of Glasgow, UK

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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SIGMM: ACM Special Interest Group on Multimedia

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Published: 01 April 2014

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ICMR '14: International Conference on Multimedia Retrieval

April 1 - 4, 2014

Glasgow, United Kingdom

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ICMR '14 Paper Acceptance Rate 21 of 111 submissions, 19%;

Overall Acceptance Rate 254 of 830 submissions, 31%

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Meena PKumar HKumar Yadav S(2023)A review on video summarization techniquesEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105667118:COnline publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1016/j.engappai.2022.105667
Gupta DSharma A(2023)A comprehensive study of automatic video summarization techniquesArtificial Intelligence Review10.1007/s10462-023-10429-z56:10(11473-11633)Online publication date: 13-Mar-2023
https://doi.org/10.1007/s10462-023-10429-z
Tiwari VBhatnagar C(2021)A survey of recent work on video summarization: approaches and techniquesMultimedia Tools and Applications10.1007/s11042-021-10977-y80:18(27187-27221)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s11042-021-10977-y
Kato KIde IDeguchi DMurase HRedi JRudinac S(2015)Generation of a Video Summary on a News Topic Based on SNS Responses to News StoriesProceedings of the Fourth International Workshop on Crowdsourcing for Multimedia10.1145/2810188.2810189(21-26)Online publication date: 30-Oct-2015
https://dl.acm.org/doi/10.1145/2810188.2810189

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Generation of a Video Summary on a News Topic Based on SNS Responses to News Stories

Threading news video topics

Modeling Latent Topics and Temporal Distance for Story Segmentation of Broadcast News

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