Publications

@article{dominguez_application_2019,

title = {Application of extractive text summarization algorithms to speech-to-text media},

author = {Víctor Domínguez and Eduardo Fidalgo and Rubel Biswas and Enrique Alegre and Laura Fernández-Robles},

url = {https://link.springer.com/chapter/10.1007/978-3-030-29859-3_46},

year  = {2019},

date = {2019-01-01},

journal = {Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, León, Spain, September 4–6, 2019, Proceedings 14},

pages = {540–550},

abstract = {This paper evaluates six extractive text summarization algorithms for speech-to-text summarization. The study assesses Luhn, TextRank, LexRank, LSA, SumBasic, and KLSum using ROUGE metrics on two datasets (DUC2001 and OWIDSum). Additionally, five speech documents from the ISCI Corpus were transcribed using Google Cloud Speech API and summarized. Results indicate that Luhn and TextRank perform best for extractive speech-to-text summarization.},

note = {Publisher: Springer International Publishing},

keywords = {AI, machine learning, natural languaje processing, speech to text, Text summarization},

pubstate = {published},

tppubtype = {article}

}