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A novel contextual topic model for multi-document summarization
- 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.
- Subjects :
- Topic model
Information retrieval
business.industry
Computer science
General Engineering
computer.software_genre
Automatic summarization
Computer Science Applications
Artificial Intelligence
Multi-document summarization
Leverage (statistics)
Artificial intelligence
business
computer
Natural language processing
Subjects
Details
- ISSN :
- 09574174
- Volume :
- 42
- Database :
- OpenAIRE
- Journal :
- Expert Systems with Applications
- Accession number :
- edsair.doi...........521e47319c7df9b85d2f1ea606be90be