A Subword Normalized Cut Approach to Automatic Story Segmentation of Chinese Broadcast News

  • Jin Zhang
  • Lei Xie
  • Wei Feng
  • Yanning Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5839)


This paper presents a subword normalized cut (N-cut) approach to automatic story segmentation of Chinese broadcast news (BN). We represent a speech recognition transcript using a weighted undirected graph, where the nodes correspond to sentences and the weights of edges describe inter-sentence similarities. Story segmentation is formalized as a graph-partitioning problem under the N-cut criterion, which simultaneously minimizes the similarity across different partitions and maximizes the similarity within each partition. We measure inter-sentence similarities and perform N-cut segmentation on the character/syllable (i.e. subword units) overlapping n-gram sequences. Our method works at the subword levels because subword matching is robust to speech recognition errors and out-of-vocabulary words. Experiments on the TDT2 Mandarin BN corpus show that syllable-bigram-based N-cut achieves the best F1-measure of 0.6911 with relative improvement of 11.52% over previous word-based N-cut that has an F1-measure of 0.6197. N-cut at the subword levels is more effective than the word level for story segmentation of noisy Chinese BN transcripts.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Jin Zhang
    • 1
  • Lei Xie
    • 1
  • Wei Feng
    • 2
  • Yanning Zhang
    • 1
  1. 1.Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Dept. of Computer Science and EngineeringThe Chinese University of Hong KongChina

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