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A novel contextual topic model for multi-document summarization

Authors :
Guangbing Yang
Dunwei Wen
Erkki Sutinen
Nian-Shing Chen
Kinshuk
Source :
Expert Systems with Applications. 42:1340-1352
Publication Year :
2015
Publisher :
Elsevier BV, 2015.

Abstract

A novel contextual topic model is proposed for multi-document summarization.The main idea is to leverage hierarchical topics and their correlations with respect to the lexical co-occurrences of words.The proposed contextual topic model can effectively determine the relevance of sentences. 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.

Details

ISSN :
09574174
Volume :
42
Database :
OpenAIRE
Journal :
Expert Systems with Applications
Accession number :
edsair.doi...........521e47319c7df9b85d2f1ea606be90be