Enhancing Sentence Ordering by Hierarchical Topic Modeling for Multi-document Summarization

  • Guangbing Yang
  • Kinshuk
  • Dunwei Wen
  • Erkki Sutinen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8265)


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.


sentence ordering hierarchical topic model text summarization machine learning 


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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Guangbing Yang
    • 1
  • Kinshuk
    • 2
  • Dunwei Wen
    • 2
  • Erkki Sutinen
    • 1
  1. 1.School of ComputingUniversity of Eastern FinlandJoensuuFinland
  2. 2.School of Computing and Information SystemsAthabasca UniversityAthabascaCanada

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