research-article

Recent advances in automatic speech summarization

Author:

Sadaoki FuruiAuthors Info & Claims

RIAO '07: Large Scale Semantic Access to Content (Text, Image, Video, and Sound)

Pages 90 - 101

Published: 30 May 2007 Publication History

Abstract

Speech summarization technology, which extracts important information and removes irrelevant information from speech, is expected to play an important role in building speech archives and improving the efficiency of spoken document retrieval. However, speech summarization has a number of significant challenges that distinguish it from general text summarization. Fundamental problems with speech summarization include speech recognition errors, disfluencies, and difficulties of sentence segmentation. Typical speech summarization systems consist of speech recognition, sentence segmentation, sentence extraction, and sentence compaction components. Most research up to now has focused on sentence extraction, using LSA (Latent Semantic Analysis), MMR (Maximal Marginal Relevance), or feature-based approaches, among which no decisive method has yet been found. Proper sentence segmentation is also essential to achieve good summarization performance. How to objectively evaluate speech summarization results is also an important issue. Several measures, including families of SumACCY and ROUGE measures, have been proposed, and correlation analyses between subjective and objective evaluation scores have been performed. Although these measures are useful for ranking various summarization methods, they do not correlate well with human evaluations, especially when spontaneous speech is targeted.

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Cited By

Ribeiro Rde Matos D(2008)Mixed-source multi-document speech-to-text summarizationProceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization10.5555/1613172.1613181(33-40)Online publication date: 23-Aug-2008
https://dl.acm.org/doi/10.5555/1613172.1613181

Index Terms

Recent advances in automatic speech summarization
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
      1. Speech recognition
2. Theory of computation
  1. Logic

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cover image ACM Other conferences

RIAO '07: Large Scale Semantic Access to Content (Text, Image, Video, and Sound)

May 2007

817 pages

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CID (France): Le Centre de Hautes Etudes Internationales D'Informatique Documentaire

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SIGIR: ACM Special Interest Group on Information Retrieval

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LE CENTRE DE HAUTES ETUDES INTERNATIONALES D'INFORMATIQUE DOCUMENTAIRE

Paris, France

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Published: 30 May 2007

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RIAO07

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CID (France)

RIAO07: Large Scale Semantic Access to Content

May 30 - June 1, 2007

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Cited By

Ribeiro Rde Matos D(2008)Mixed-source multi-document speech-to-text summarizationProceedings of the Workshop on Multi-source Multilingual Information Extraction and Summarization10.5555/1613172.1613181(33-40)Online publication date: 23-Aug-2008
https://dl.acm.org/doi/10.5555/1613172.1613181

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