Leveraging BERT for Extractive Text Summarization on Lectures

Understand

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.