Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition

Yang Yen Ou; Ta Wen Kuan; Anand Paul; Jhing Fa Wang; An Chao Tsai

doi:10.1007/s11704-016-6190-2

Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition

Yang Yen Ou, Ta Wen Kuan, Anand Paul, Jhing Fa Wang, An Chao Tsai

Research output: Contribution to journal › Article › peer-review

1 Scopus citations

Abstract

This work presents a spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of automatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possibility and potential of the proposed system.

Original language	English
Pages (from-to)	429-443
Number of pages	15
Journal	Frontiers of Computer Science
Volume	11
Issue number	3
DOIs	https://doi.org/10.1007/s11704-016-6190-2
State	Published - 1 Jun 2017

Keywords

keyword extraction
natural language processing (NLP)
sentiment analysis
spoken dialog summarization

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10.1007/s11704-016-6190-2

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title = "Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition",

abstract = "This work presents a spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of automatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possibility and potential of the proposed system.",

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author = "Ou, {Yang Yen} and Kuan, {Ta Wen} and Anand Paul and Wang, {Jhing Fa} and Tsai, {An Chao}",

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AU - Ou, Yang Yen

AU - Kuan, Ta Wen

AU - Paul, Anand

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AU - Tsai, An Chao

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N2 - This work presents a spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of automatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possibility and potential of the proposed system.

AB - This work presents a spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition. The semantic content is compressed and classified by factor categories from spoken dialog. The transcription of automatic speech recognition is then processed through Chinese Knowledge and Information Processing segmentation system. The proposed system also adopts the part-of-speech tags to effectively select and rank the keywords. Finally, the HAPPINESS/SUFFERING factor recognition is done by the proposed point-wise mutual information. Compared with the original method, the performance is improved by applying the significant scores of keywords. The experimental results show that the average precision rate for factor recognition in outside test can reach 73.5% which demonstrates the possibility and potential of the proposed system.

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Spoken dialog summarization system with HAPPINESS/SUFFERING factor recognition

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Keywords

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