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

  1. Domínguez M., Victor 1
  2. Fidalgo F., Eduardo 12
  3. Rubel Biswas 12
  4. Enrique Alegre 12
  5. Laura Fernández-Robles 12
  1. 1 Universidad de León
    info

    Universidad de León

    León, España

    ROR https://ror.org/02tzt0b78

  2. 2 INCIBE (Spanish National Institute of Cybersecurity, León)
Libro:
Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings
  1. Hilde Pérez García (coord.)
  2. Lidia Sánchez González (coord.)
  3. Manuel Castejón Limas (coord.)
  4. Héctor Quintián Pardo (coord.)
  5. Emilio Corchado Rodríguez (coord.)

Editorial: Springer Suiza

ISBN: 978-3-030-29859-3,  978-3-030-29858-6

Año de publicación: 2019

Páginas: 540-550

Congreso: Hybrid Artificial Intelligent Systems (14. 2019. León)

Tipo: Aportación congreso

DIALNET GOOGLE SCHOLAR

Resumen

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.

Fuente de los datos: Dialnet