IEICE TRANSACTIONS on Information
A Hybrid Topic Model for Multi-Document Summarization
Summary :
Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.5 pp.1089-1094
- Publication Date
- 2015/05/01
- Publicized
- 2015/02/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2014EDP7229
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
Authors
JinAn XU
Beijing Jiaotong University
JiangMing LIU
Beijing Jiaotong University
Kenji ARAKI
Hokkaido University
Keyword
Latest Issue
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JinAn XU, JiangMing LIU, Kenji ARAKI, "A Hybrid Topic Model for Multi-Document Summarization" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 5, pp. 1089-1094, May 2015, doi: 10.1587/transinf.2014EDP7229.
Abstract: Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2014EDP7229/_p
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@ARTICLE{e98-d_5_1089,
author={JinAn XU, JiangMing LIU, Kenji ARAKI, },
journal={IEICE TRANSACTIONS on Information},
title={A Hybrid Topic Model for Multi-Document Summarization},
year={2015},
volume={E98-D},
number={5},
pages={1089-1094},
abstract={Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.},
keywords={},
doi={10.1587/transinf.2014EDP7229},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - A Hybrid Topic Model for Multi-Document Summarization
T2 - IEICE TRANSACTIONS on Information
SP - 1089
EP - 1094
AU - JinAn XU
AU - JiangMing LIU
AU - Kenji ARAKI
PY - 2015
DO - 10.1587/transinf.2014EDP7229
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2015
AB - Topic features are useful in improving text summarization. However, independency among topics is a strong restriction on most topic models, and alleviating this restriction can deeply capture text structure. This paper proposes a hybrid topic model to generate multi-document summaries using a combination of the Hidden Topic Markov Model (HTMM), the surface texture model and the topic transition model. Based on the topic transition model, regular topic transition probability is used during generating summary. This approach eliminates the topic independence assumption in the Latent Dirichlet Allocation (LDA) model. Meanwhile, the results of experiments show the advantage of the combination of the three kinds of models. This paper includes alleviating topic independency, and integrating surface texture and shallow semantic in documents to improve summarization. In short, this paper attempts to realize an advanced summarization system.
ER -
English
