Sentence modeling for extractive speech summarization

@article{Chen2013SentenceMF,
  title={Sentence modeling for extractive speech summarization},
  author={Berlin Chen and Hao-Chin Chang and Kuan-Yu Chen},
  journal={2013 IEEE International Conference on Multimedia and Expo (ICME)},
  year={2013},
  pages={1-6}
}
Extractive speech summarization, aiming to select an indicative set of sentences from a spoken document so as to concisely represent the most important aspects of the document, has emerged as an attractive area of research and experimentation. A recent school of thought is to employ the language modeling (LM) framework along with the Kullback-Leibler (KL) divergence measure for important sentence selection, which has shown preliminary promise for extractive speech summarization. Our work in… CONTINUE READING

From This Paper

Figures, tables, and topics from this paper.

Explore Further: Topics Discussed in This Paper

Citations

Publications citing this paper.
SHOWING 1-10 OF 11 CITATIONS

Similar Papers

Loading similar papers…