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2019 | OriginalPaper | Chapter

Application of Extractive Text Summarization Algorithms to Speech-to-Text Media

Authors : Domínguez M. Victor, Fidalgo F. Eduardo, Rubel Biswas, Enrique Alegre, Laura Fernández-Robles

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

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Abstract

This paper presents how speech-to-text summarization can be performed using extractive text summarization algorithms. Our objective is to make a recommendation about which of the six text summary algorithms evaluated in the study is the most suitable for the task of audio summarization. First, we have selected six text summarization algorithms: Luhn, TextRank, LexRank, LSA, SumBasic, and KLSum. Then, we have evaluated them on two datasets, DUC2001 and OWIDSum, with six ROUGE metrics. After that, we have selected five speech documents from ISCI Corpus dataset, and we have transcribed using the Automatic Speech Recognition (ASR) from Google Cloud Speech API. Finally, we applied the studied extractive summarization algorithms to these five text samples to obtain a text summary from the original audio file. Experimental results showed that Luhn and TextRank obtained the best performance for the task of extractive speech-to-text summarization on the samples evaluated.

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Metadata
Title
Application of Extractive Text Summarization Algorithms to Speech-to-Text Media
Authors
Domínguez M. Victor
Fidalgo F. Eduardo
Rubel Biswas
Enrique Alegre
Laura Fernández-Robles
Copyright Year
2019
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-030-29858-6

Electronic ISBN: 978-3-030-29859-3

Copyright Year: 2019

DOI
https://doi.org/10.1007/978-3-030-29859-3_46

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