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Korean text summarization using an aggregate similarity

Published: 01 November 2000 Publication History

Abstract

In this paper, each document is represented by a weighted graph called a text relationship map. In the graph, each node represents a vector of nouns in a sentence, an undirected link connects two nodes if two sentences are semantically related, and a weight on the link is a value of the similarity between a pair of sentences. The vector similarity can be computed as the inner product between corresponding vector elements. The similarity is based on the word overlap between the corresponding sentences. The importance of a node on the map, called an aggregate similarity, is defined as the sum of weights on the links connecting it to other nodes on the map. In this paper, we present a Korean text summarization system using the aggregate similarity. To evaluate our system, we used two test collections: one collection (PAPER-InCon) consists of 100 papers in the domain of computer science; the other collection (NEWS) is composed of 105 articles in the newspapers. Under the compression rate of 20%, we achieved the recall of 46.6% (PAPER-InCon) and 30.5% (NEWS), and the precision of 76.9% (PAPER-InCon) and 42.3% (NEWS). Experiments show that our system outperforms two commercial systems.

References

[1]
Cowie, J., Mahesh, K., Nirenburg, S. and Zajac, R. MINDS - multilingual interactive document summarization. In Working Notes of the AAAI Spring Symposium on Intelligent Text Summarization, Spring. 1998, pp. 131-132.
[2]
Jang. D. and Myaeng, S.-H. Automatic text summarization systems. Korea Information Science Society Review. 1997, 15(10), pp.42-49.
[3]
Salton, G, Singhal, A., Mitra, M. and Buckly, C. Automatic text structuring and summarization. In Advances in Automatic Text Summarization, Eds. Mani, I. and Maybury, M. T. The MIT Press, 1999, pp. 61-70
[4]
Mani, I. and Maybury, M. T. Advanced in Automatic Text Summarization. The MIT Press, 1999.
[5]
Sparck Jones, K. Automatic summarizing: factors and directions, In Advances in Automatic Text Summarization, Eds. Mani, I. and Maybury, M. T. The MIT Press, 1999, pp. 1-12.
[6]
Aone, C., Gorlinsky, J., Larsen, B., and Okurowski, M. E. A trainable summarizer with knowledge acquired from robust NLP techniques. In Advances in Automatic Text Summarization, Eds. Mani, I. and Maybury, M. T. The MIT Press, 1999, pp. 71-80.
[7]
Kupiec, J., Pedersen, J., and Chen, F. A trainable document summarizer, in Proceedings of the 18 th ACM-SIGIR Conference. 1995, pp. 68-73.
[8]
Myaeng, S. H. and Jang, D. Development and evaluation of a statistically based document summarization system. In Advances in Automatic Text Summarization, Eds. Mani, I. and Maybury, M. T. The MIT Press, 1999, pp. 61-70.
[9]
Kang, S.-B. Implementation of a summarization system using statistical information of Korean documents. Master's thesis, Pusan National University, Department of Computer Science, 1997.
[10]
Lee, M.-H., Park, M.-S., Kim, M.-J., and Lee, S.-J. Sentence extraction using document features and heading. In Proceedings of KIPS. 1997, 6(2), pp. AI41-AI45.
[11]
Ryu, D.-W. and J.-H. Lee. Word co-occurrence based automatic text summarization. In Proceedings of KISS. 2000, 27(1), pp. 345-347.
[12]
Kim, Y.-K. and Kwon, H.-C. Noun extraction system in information retrieval system of MIRINE, In Proceedings the 1 st Workshop on the Evaluation for Morphological Analyzer and Part-of-Speech Tagging System, 1999, pp. 89-91.
[13]
Won, H., Park, M. and Lee. G. Integrated indexing method using compound noun segmentation and noun phrase synthesis. Journal of KISS: Software and Applications. 2000, 27(1), pp. 84-95.
[14]
Kim, J.-H. Korean part-of-speech tagging using a weighted network. Journal of KISS (B) : Software and Applications. 1998, 25(6), pp. 951-959.
[15]
Maosong, S., Dayang, S. and Tsou, B. K. Chinese word segmentation without using lexicon and handcrafted training data. In Proceedings of COLING- ACL 98, 1998, pp. 1265-1271.
[16]
Aho, A. V. and Ullman, J. D. The Theory of Parsing, Translation, and Compiling, Prentice-Hall, 1973.
[17]
Kim, T.-H., Park, H.-R., Shin, J.-.H. A study on text understanding model for retrieval/summarization/ filtering. In Proceedings of the Workshop on Softscience, 1999.
[18]
Baeza-Yates, R. and Ribeiro-Neto, B. Modem Information Retrieval. Addison Wesley, 1999.

Cited By

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  • (2019)Cross-Lingual Korean Speech-to-Text SummarizationIntelligent Information and Database Systems10.1007/978-3-030-14799-0_17(198-206)Online publication date: 7-Mar-2019
  • (2018)Automatic Arabic text summarisation system AATSS based on morphological analysisInternational Journal of Intelligent Systems Technologies and Applications10.1504/IJISTA.2018.09400717:3(272-280)Online publication date: 1-Jan-2018
  • (2017)Efficient Korean text summarization based on key phrase extraction2017 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC.2017.8107743(61-66)Online publication date: Jul-2017
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Published In

cover image ACM Conferences
IRAL '00: Proceedings of the fifth international workshop on on Information retrieval with Asian languages
November 2000
220 pages
ISBN:1581133006
DOI:10.1145/355214
  • Chairmen:
  • Kam-Fai Wong,
  • Dik L. Lee,
  • Jong-Hyeok Lee
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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Published: 01 November 2000

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Author Tags

  1. Korean noun extraction
  2. Korean text summarization
  3. aggregate similarity

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Cited By

View all
  • (2019)Cross-Lingual Korean Speech-to-Text SummarizationIntelligent Information and Database Systems10.1007/978-3-030-14799-0_17(198-206)Online publication date: 7-Mar-2019
  • (2018)Automatic Arabic text summarisation system AATSS based on morphological analysisInternational Journal of Intelligent Systems Technologies and Applications10.1504/IJISTA.2018.09400717:3(272-280)Online publication date: 1-Jan-2018
  • (2017)Efficient Korean text summarization based on key phrase extraction2017 International Conference on Machine Learning and Cybernetics (ICMLC)10.1109/ICMLC.2017.8107743(61-66)Online publication date: Jul-2017
  • (2017)An investigation on graphical approach for tamil text summary generation2017 International Conference on Intelligent Computing and Control (I2C2)10.1109/I2C2.2017.8321805(1-5)Online publication date: Jun-2017
  • (2017)POS-Tagging Enhanced Korean Text SummarizationIntelligent Computing Methodologies10.1007/978-3-319-63315-2_37(425-435)Online publication date: 21-Jul-2017
  • (2016)How to Improve Text Summarization and Classification by Mutual Cooperation on an Integrated FrameworkExpert Systems with Applications: An International Journal10.1016/j.eswa.2016.05.00160:C(222-233)Online publication date: 30-Oct-2016
  • (2015)Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment FeaturesInternational Journal of Information Retrieval Research10.4018/IJIRR.20151001045:4(46-70)Online publication date: 1-Oct-2015
  • (2008)An effective sentence-extraction technique using contextual information and statistical approaches for text summarizationPattern Recognition Letters10.1016/j.patrec.2008.02.00829:9(1366-1371)Online publication date: Jul-2008
  • (2005)Text summarization using a trainable summarizer and latent semantic analysisInformation Processing and Management: an International Journal10.1016/j.ipm.2004.04.00341:1(75-95)Online publication date: 1-Jan-2005
  • (2002)Chinese Text Summarization Using a Trainable Summarizer and Latent Semantic AnalysisDigital Libraries: People, Knowledge, and Technology10.1007/3-540-36227-4_8(76-87)Online publication date: 16-Dec-2002

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