Topic Development Based Refinement of Audio-Segmented Television News

  • Alfredo Favenza
  • Mario Cataldi
  • Maria Luisa Sapino
  • Alberto Messina
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5039)


With the advent of the cable based television model, there is an emerging requirement for random access capabilities, from a variety of media channels, such as smart terminals and Internet. Random access to the information within a newscast program requires appropriate segmentation of the news. We present text analysis based techniques on the transcript of the news, to refine the automatic audio-visual segmentation. We present the effectiveness of applying the text segmentation algorithm CUTS to the news segmentation domain. We propose two extensions to the algorithm, and show their impacts through an initial evaluation.


Automatic Speech Recognition Curve Segment Topic Development Text Stream Unit Duration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Alfredo Favenza
    • 1
  • Mario Cataldi
    • 1
  • Maria Luisa Sapino
    • 1
  • Alberto Messina
    • 2
  1. 1.Universita’ di TorinoItaly
  2. 2.RAI - Centre for Research and Technological InnovationTorinoItaly

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