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Published in:
2015 | OriginalPaper | Chapter
A Hierarchical Tree Model for Update Summarization
Authors : Rumeng Li, Hiroyuki Shindo
Published in: Advances in Information Retrieval
Publisher: Springer International Publishing
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Update summarization is a new challenge which combines salience ranking with novelty detection. This paper presents a generative hierarchical tree model (HTM for short) based on Hierarchical Latent Dirichlet Allocation (hLDA) to discover the topic structure within history dataset and update dataset. From the tree structure, we can clearly identify the diversity and commonality between history dataset and update dataset. A summary ranking approach is proposed based on such structure by considering different aspects such as focus, novelty and non-redundancy. Experimental results show the effectiveness of our model.