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A genetic graph-based clustering approach to biomedical summarization

Authors:

Héctor D. Menéndez,

David CamachoAuthors Info & Claims

WIMS '13: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics

Article No.: 10, Pages 1 - 8

https://doi.org/10.1145/2479787.2479807

Published: 12 June 2013 Publication History

Abstract

Summarization techniques have become increasingly important over the last few years, specially in biomedical research, where information overload is major problem. Researchers of this area need a shorter version of the texts which contains all the important information while discarding irrelevant one. There are several applications which deal with this problem, however, these applications are sometimes less informative than the user needs. This work deals with this problem trying to improve a summarization graph-based process using genetic clustering techniques. Our automatic summaries are compared to those produced by several commercial and research summarizers, and demonstrate the appropriateness of using genetic techniques in automatic summarization.

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Cited By

Davoodijam EGhadiri NLotfi Shahreza MRinaldi F(2022)MultiGBSJournal of Biomedical Informatics10.1016/j.jbi.2021.103706116:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.jbi.2021.103706
García-Cobo IMenéndez H(2021)Designing large quantum key distribution networks via medoid-based algorithmsFuture Generation Computer Systems10.1016/j.future.2020.09.037115(814-824)Online publication date: Feb-2021
https://doi.org/10.1016/j.future.2020.09.037
Rouane OBelhadef HBouakkaz M(2019)Word Embedding-Based Biomedical Text SummarizationEmerging Trends in Intelligent Computing and Informatics10.1007/978-3-030-33582-3_28(288-297)Online publication date: 2-Nov-2019
https://doi.org/10.1007/978-3-030-33582-3_28
Show More Cited By

Index Terms

A genetic graph-based clustering approach to biomedical summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing

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Information & Contributors

Information

Published In

cover image ACM Other conferences

WIMS '13: Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics

June 2013

408 pages

ISBN:9781450318501

DOI:10.1145/2479787

Conference Chair:
David Camacho
Autonomous University of Madrid, Spain
,
Program Chairs:
Rajendra Akerkar
Western Norway Research Institute, Norway
,
Maria D. Rodriguez Moreno
University of Alcalá, Spain

Copyright © 2013 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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UAM: Autonomous University of Madrid

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New York, NY, United States

Publication History

Published: 12 June 2013

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Ministerio de Educación, Cultura y Deporte

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WIMS '13

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UAM

WIMS '13: 3rd International Conference on Web Intelligence, Mining and Semantics

June 12 - 14, 2013

Madrid, Spain

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WIMS '13 Paper Acceptance Rate 28 of 72 submissions, 39%;

Overall Acceptance Rate 140 of 278 submissions, 50%

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Cited By

Davoodijam EGhadiri NLotfi Shahreza MRinaldi F(2022)MultiGBSJournal of Biomedical Informatics10.1016/j.jbi.2021.103706116:COnline publication date: 22-Apr-2022
https://dl.acm.org/doi/10.1016/j.jbi.2021.103706
García-Cobo IMenéndez H(2021)Designing large quantum key distribution networks via medoid-based algorithmsFuture Generation Computer Systems10.1016/j.future.2020.09.037115(814-824)Online publication date: Feb-2021
https://doi.org/10.1016/j.future.2020.09.037
Rouane OBelhadef HBouakkaz M(2019)Word Embedding-Based Biomedical Text SummarizationEmerging Trends in Intelligent Computing and Informatics10.1007/978-3-030-33582-3_28(288-297)Online publication date: 2-Nov-2019
https://doi.org/10.1007/978-3-030-33582-3_28
Rouane OBelhadef HBouakkaz M(2019)A New Biomedical Text Summarization Method Based on Sentence Clustering and Frequent Itemsets MiningProceedings of the 8th International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT’18), Vol.110.1007/978-3-030-21005-2_14(144-152)Online publication date: 11-Jul-2019
https://doi.org/10.1007/978-3-030-21005-2_14
Moradi MGhadiri N(2018)Different approaches for identifying important concepts in probabilistic biomedical text summarizationArtificial Intelligence in Medicine10.1016/j.artmed.2017.11.00484(101-116)Online publication date: Jan-2018
https://doi.org/10.1016/j.artmed.2017.11.004
Nasr Azadani MGhadiri N(2018)Evaluating Different Similarity Measures for Automatic Biomedical Text SummarizationIntelligent Systems Design and Applications10.1007/978-3-319-76348-4_30(305-314)Online publication date: 22-Mar-2018
https://doi.org/10.1007/978-3-319-76348-4_30
Menendez HPlaza LCamacho D(2014)Combining graph connectivity and genetic clustering to improve biomedical summarization2014 IEEE Congress on Evolutionary Computation (CEC)10.1109/CEC.2014.6900370(2740-2747)Online publication date: Jul-2014
https://doi.org/10.1109/CEC.2014.6900370

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