  In the last two decades, automatic extractive text summarization on lectures
has demonstrated to be a useful tool for collecting key phrases and sentences
that best represent the content. However, many current approaches utilize dated
approaches, producing sub-par outputs or requiring several hours of manual
tuning to produce meaningful results. Recently, new machine learning
architectures have provided mechanisms for extractive summarization through the
clustering of output embeddings from deep learning models. This paper reports
on the project called Lecture Summarization Service, a python based RESTful
service that utilizes the BERT model for text embeddings and KMeans clustering
to identify sentences closes to the centroid for summary selection. The purpose
of the service was to provide students a utility that could summarize lecture
content, based on their desired number of sentences. On top of the summary
work, the service also includes lecture and summary management, storing content
on the cloud which can be used for collaboration. While the results of
utilizing BERT for extractive summarization were promising, there were still
areas where the model struggled, providing feature research opportunities for
further improvement.
