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Clustering to find exemplar terms for keyphrase extraction

Authors:

Maosong SunAuthors Info & Claims

EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1

Pages 257 - 266

Published: 06 August 2009 Publication History

Abstract

Keyphrases are widely used as a brief summary of documents. Since manual assignment is time-consuming, various unsupervised ranking methods based on importance scores are proposed for keyphrase extraction. In practice, the keyphrases of a document should not only be statistically important in the document, but also have a good coverage of the document. Based on this observation, we propose an unsupervised method for keyphrase extraction. Firstly, the method finds exemplar terms by leveraging clustering techniques, which guarantees the document to be semantically covered by these exemplar terms. Then the keyphrases are extracted from the document using the exemplar terms. Our method outperforms sate-of-the-art graph-based ranking methods (TextRank) by 9.5% in F1-measure.

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Cited By

Liu YWu HHuang ZWang HNing YMa JLiu QChen E(2023)TechPat: Technical Phrase Extraction for Patent MiningACM Transactions on Knowledge Discovery from Data10.1145/359660317:9(1-31)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3596603
Sun YLi DWu YWan XHuang C(2022)WAIN: Automatic Web Application Identification and Naming MethodProceedings of the 13th Asia-Pacific Symposium on Internetware10.1145/3545258.3545271(37-44)Online publication date: 11-Jun-2022
https://dl.acm.org/doi/10.1145/3545258.3545271
Deka PJurek-Loughrey ADeepak (2021)Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact CheckingThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487701(267-277)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487701
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Clustering to find exemplar terms for keyphrase extraction
1. Computing methodologies

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Published In

cover image DL Hosted proceedings

EMNLP '09: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1

August 2009

505 pages

ISBN:9781932432596

Program Chairs:
Philipp Koehn
University of Edinburgh
,
Rada Mihalcea
University of North Texas

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 06 August 2009

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Overall Acceptance Rate 73 of 234 submissions, 31%

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Cited By

Liu YWu HHuang ZWang HNing YMa JLiu QChen E(2023)TechPat: Technical Phrase Extraction for Patent MiningACM Transactions on Knowledge Discovery from Data10.1145/359660317:9(1-31)Online publication date: 15-Jun-2023
https://dl.acm.org/doi/10.1145/3596603
Sun YLi DWu YWan XHuang C(2022)WAIN: Automatic Web Application Identification and Naming MethodProceedings of the 13th Asia-Pacific Symposium on Internetware10.1145/3545258.3545271(37-44)Online publication date: 11-Jun-2022
https://dl.acm.org/doi/10.1145/3545258.3545271
Deka PJurek-Loughrey ADeepak (2021)Unsupervised Keyword Combination Query Generation from Online Health Related Content for Evidence-Based Fact CheckingThe 23rd International Conference on Information Integration and Web Intelligence10.1145/3487664.3487701(267-277)Online publication date: 29-Nov-2021
https://dl.acm.org/doi/10.1145/3487664.3487701
El Akrouchi MBenbrahim HKassou I(2021)Review on adopting concept extraction in weak signals detection in competitive intelligenceThe 7th Annual International Conference on Arab Women in Computing in Conjunction with the 2nd Forum of Women in Research10.1145/3485557.3485560(1-8)Online publication date: 25-Aug-2021
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Wang HYe JYu ZWang JMao C(2020)Unsupervised Keyword Extraction Methods Based on a Word Graph NetworkInternational Journal of Ambient Computing and Intelligence10.4018/IJACI.202004010411:2(68-79)Online publication date: 1-Apr-2020
https://dl.acm.org/doi/10.4018/IJACI.2020040104
Albahr AChe DAlbahar M(2019)SemKeyphrase: An Unsupervised Approach to Keyphrase Extraction from MOOC Video LecturesIEEE/WIC/ACM International Conference on Web Intelligence10.1145/3350546.3352535(303-307)Online publication date: 14-Oct-2019
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Han FNiu DLai KGuo WHe YXu Y(2019)Inferring Search Queries from Web Documents via a Graph-Augmented Sequence to Attention NetworkThe World Wide Web Conference10.1145/3308558.3313746(2792-2798)Online publication date: 13-May-2019
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Ray Chowdhury JCaragea CCaragea D(2019)Keyphrase Extraction from Disaster-related TweetsThe World Wide Web Conference10.1145/3308558.3313696(1555-1566)Online publication date: 13-May-2019
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Alzaidy RCaragea CGiles C(2019)Bi-LSTM-CRF Sequence Labeling for Keyphrase Extraction from Scholarly DocumentsThe World Wide Web Conference10.1145/3308558.3313642(2551-2557)Online publication date: 13-May-2019
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Gero ZHo JShi XBuck MMa JVeltri P(2019)NamedKeysProceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics10.1145/3307339.3342147(328-337)Online publication date: 4-Sep-2019
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