Automatic Generation of Learning Objects Using Text Summarizer Based on Deep Learning Models

• Leandro Massetti Ribeiro Oliveira UFMA http://orcid.org/0000-0002-0097-5161
• Antonio José G. Busson PUC-Rio http://orcid.org/0000-0001-5394-0707
• Carlos de Salles S. Neto UFMA http://orcid.org/0000-0002-6800-1881
• Gabriel N. P. dos Santos PUC-Rio
• Sérgio Colcher PUC-Rio

DOI: https://doi.org/10.5753/sbie.2021.217360

Resumo

A learning object (LO) is an entity, digital or not, that can be used and reused or referenced during a technological support process for teaching and learning. Despite mainly being multimedia, with audio, video, text and images synchronized with each other, LOs can help disseminate knowledge even only in educational texts. However, creating these texts can be costly in time and effort, creating the need to seek new ways to generate this content. This article presents a solution for the generation of text-based LOs generated through summaries supported by Deep Learning models. The present work was evaluated in a supervised experiment in which volunteers rate computer educational texts generated by three types of summarizers. The results presented are positive and allow us to compare the performance of summaries as LO generators in text format. The findings also suggest that using post-processing in the output of models can improve the readability of generated content.

Palavras-chave: Learning Objects, Text summarizer, Deep Learning

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