TY - JOUR
T1 - A novel contextual topic model for multi-document summarization
AU - Yang, Guangbing
AU - Wen, Dunwei
AU - Kinshuk,
AU - Chen, Nian Shing
AU - Sutinen, Erkki
N1 - Publisher Copyright:
©2014 Elsevier Ltd. All rights reserved.
PY - 2015/2/15
Y1 - 2015/2/15
N2 - Information overload becomes a serious problem in the digital age. It negatively impacts understanding of useful information. How to alleviate this problem is the main concern of research on natural language processing, especially multi-document summarization. With the aim of seeking a new method to help justify the importance of similar sentences in multi-document summarizations, this study proposes a novel approach based on recent hierarchical Bayesian topic models. The proposed model incorporates the concepts of n-grams into hierarchically latent topics to capture the word dependencies that appear in the local context of a word. The quantitative and qualitative evaluation results show that this model has outperformed both hLDA and LDA in document modeling. In addition, the experimental results in practice demonstrate that our summarization system implementing this model can significantly improve the performance and make it comparable to the state-of-the-art summarization systems.
AB - Information overload becomes a serious problem in the digital age. It negatively impacts understanding of useful information. How to alleviate this problem is the main concern of research on natural language processing, especially multi-document summarization. With the aim of seeking a new method to help justify the importance of similar sentences in multi-document summarizations, this study proposes a novel approach based on recent hierarchical Bayesian topic models. The proposed model incorporates the concepts of n-grams into hierarchically latent topics to capture the word dependencies that appear in the local context of a word. The quantitative and qualitative evaluation results show that this model has outperformed both hLDA and LDA in document modeling. In addition, the experimental results in practice demonstrate that our summarization system implementing this model can significantly improve the performance and make it comparable to the state-of-the-art summarization systems.
KW - Contextual topic
KW - Hierarchical topic model
KW - Multi-document summarization
UR - http://www.scopus.com/inward/record.url?scp=84908108338&partnerID=8YFLogxK
U2 - 10.1016/j.eswa.2014.09.015
DO - 10.1016/j.eswa.2014.09.015
M3 - Journal Article
AN - SCOPUS:84908108338
SN - 0957-4174
VL - 42
SP - 1340
EP - 1352
JO - Expert Systems with Applications
JF - Expert Systems with Applications
IS - 3
ER -