IEICE TRANSACTIONS on Information
A Probabilistic Sentence Reduction Using Maximum Entropy Model
Summary :
This paper describes a new probabilistic sentence reduction method using maximum entropy model. In contrast to previous methods, the proposed method has the ability to produce multiple best results for a given sentence, which is useful in text summarization applications. Experimental results show that the proposed method improves on earlier methods in both accuracy and computation time.
- Publication
- IEICE TRANSACTIONS on Information Vol.E88-D No.2 pp.278-288
- Publication Date
- 2005/02/01
- Publicized
- Online ISSN
- DOI
- 10.1093/ietisy/e88-d.2.278
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
Keyword
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Minh LE NGUYEN, Masaru FUKUSHI, Susumu HORIGUCHI, "A Probabilistic Sentence Reduction Using Maximum Entropy Model" in IEICE TRANSACTIONS on Information,
vol. E88-D, no. 2, pp. 278-288, February 2005, doi: 10.1093/ietisy/e88-d.2.278.
Abstract: This paper describes a new probabilistic sentence reduction method using maximum entropy model. In contrast to previous methods, the proposed method has the ability to produce multiple best results for a given sentence, which is useful in text summarization applications. Experimental results show that the proposed method improves on earlier methods in both accuracy and computation time.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e88-d.2.278/_p
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@ARTICLE{e88-d_2_278,
author={Minh LE NGUYEN, Masaru FUKUSHI, Susumu HORIGUCHI, },
journal={IEICE TRANSACTIONS on Information},
title={A Probabilistic Sentence Reduction Using Maximum Entropy Model},
year={2005},
volume={E88-D},
number={2},
pages={278-288},
abstract={This paper describes a new probabilistic sentence reduction method using maximum entropy model. In contrast to previous methods, the proposed method has the ability to produce multiple best results for a given sentence, which is useful in text summarization applications. Experimental results show that the proposed method improves on earlier methods in both accuracy and computation time.},
keywords={},
doi={10.1093/ietisy/e88-d.2.278},
ISSN={},
month={February},}
Copy
TY - JOUR
TI - A Probabilistic Sentence Reduction Using Maximum Entropy Model
T2 - IEICE TRANSACTIONS on Information
SP - 278
EP - 288
AU - Minh LE NGUYEN
AU - Masaru FUKUSHI
AU - Susumu HORIGUCHI
PY - 2005
DO - 10.1093/ietisy/e88-d.2.278
JO - IEICE TRANSACTIONS on Information
SN -
VL - E88-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2005
AB - This paper describes a new probabilistic sentence reduction method using maximum entropy model. In contrast to previous methods, the proposed method has the ability to produce multiple best results for a given sentence, which is useful in text summarization applications. Experimental results show that the proposed method improves on earlier methods in both accuracy and computation time.
ER -
English
