The sentence ordering is a difficult but very important task in multi-document summarization. With the aim of producing a coherent and legible summary for multiple documents, this study proposes a novel approach that is built upon a hierarchical topic model for automatic evaluation of sentence ordering. By learning topic correlations from the topic hierarchies, this model is able to automatically evaluate sentences to find a plausible order to arrange them for generating a more readable summary. The experimental results demonstrate that our proposed approach can improve the summarization performance and present a significant enhancement on the sentence ordering for multi-document summarization. In addition, the experimental results show that our model can automatically analyze the topic relationships to infer a strategy for sentence ordering. Human evaluations justify that the generated summaries, which implement this strategy, demonstrate a good linguistic performance in terms of coherence, readability, and redundancy. © Springer-Verlag 2013.
CITATION STYLE
Yang, G., Kinshuk, Wen, D., & Sutinen, E. (2013). Enhancing sentence ordering by hierarchical topic modeling for multi-document summarization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8265 LNAI, pp. 367–379). https://doi.org/10.1007/978-3-642-45114-0_30
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