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

• Domínguez M., Victor ^[1] ; Fidalgo F., Eduardo ^{[1] [2]} ; Rubel Biswas ^{[1] [2]} ; Enrique Alegre ^{[1] [2]} ; Laura Fernández-Robles ^{[2] [1]}
  1. [1] Universidad de León

     Universidad de León

     León, España

     
  2. [2] INCIBE (Spanish National Institute of Cybersecurity, León)
• Localización: Hybrid Artificial Intelligent Systems. 14th International Conference, HAIS 2019: León, Spain, September 4–6, 2019. Proceedings / coord. por Hilde Pérez García, Lidia Sánchez González, Manuel Castejón Limas, Héctor Quintián Pardo, Emilio Santiago Corchado Rodríguez, 2019, ISBN 978-3-030-29858-6, págs. 540-550
• Idioma: inglés
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  • 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.