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Automatic Summarization of Highly Spontaneous Speech

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Speech and Computer (SPECOM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9811))

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Abstract

This paper addresses speech summarization of highly spontaneous speech. Speech is converted into text using an ASR, then segmented into tokens. Human made and automatic, prosody based tokenization are compared. The obtained sentence-like units are analysed by a syntactic parser to help automatic sentence selection for the summary. The preprocessed sentences are ranked based on thematic terms and sentence position. The thematic term is expressed in two ways: TF-IDF and Latent Semantic Indexing. The sentence score is calculated as linear combination of the thematic term score and a sentence position score. To generate the summary, the top 10 candidates for the most informative/best summarizing sentences are selected. The system performance showed comparable results (recall: 0.62, precision: 0.79 and F-measure 0.68) with the prosody based tokenization approach. A subjective test is also carried out on a Likert scale.

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Acknowledgment

The authors would like to thank the support of the Hungarian National Innovation Office (NKFI) under contract IDs PD-112598 and PD-108762.

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Authors and Affiliations

  1. Research Institute for Linguistics, Hungarian Academy of Sciences, Budapest, Hungary

    András Beke

  2. Department of Telecommunications and Media Informatics, Budapest University of Technology and Economics, Budapest, Hungary

    György Szaszák

Authors
  1. András Beke
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  2. György Szaszák
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Corresponding author

Correspondence to György Szaszák .

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Editors and Affiliations

  1. SPIIRAS , Saint-Petersburg, Russia

    Andrey Ronzhin

  2. Moscow State Linguistic University , Moscow, Russia

    Rodmonga Potapova

  3. Budapest University of Technology and Economics, Budapest, Hungary

    Géza Németh

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Beke, A., Szaszák, G. (2016). Automatic Summarization of Highly Spontaneous Speech. In: Ronzhin, A., Potapova, R., Németh, G. (eds) Speech and Computer. SPECOM 2016. Lecture Notes in Computer Science(), vol 9811. Springer, Cham. https://doi.org/10.1007/978-3-319-43958-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-43958-7_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43957-0

  • Online ISBN: 978-3-319-43958-7

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