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A Hybrid Topic Model for Multi-Document Summarization
JinAn XU JiangMing LIU Kenji ARAKI
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2015/05/01
Online ISSN: 1745-1361
Type of Manuscript: PAPER
Category: Natural Language Processing
multi-document summarization, hybrid topic model, hidden topic Markov model (HTMM), surface texture model, topic transition model,
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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.