open access

https://hdl.handle.net/2115/59626

A Hybrid Topic Model for Multi-Document Summarization

英語

topic transition model

2015-05

The Institute of Electronics, Information and Communication Engineers (IEICE)

IEICE transactions on information and systems

E98D(5)

1089-1094

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.

学術雑誌論文

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Copyright ©2015 The Institute of Electronics, Information and Communication Engineers

1745-1361