Filtering Videos By the Text Summarization technique based on BERT Model

Abstract

The research of multimedia search has grown great attention to many researchers in recent years multimedia needs to provide a fast and accurate search method This research proposes a system to realize a video summary by using an automatic text summarization People usually read faster than watch videos therefor a text summarization is used as the multimedia search method in this research Automatic text summarization is one of natural language processing and aims to extract the key points from the text In recent years the research of text summarization has a huge process because deep learning has been applied in it Deep learning learned from a great amount of training data and applied the leaning models on the text to stress the essentials This study applies the BERT model (Bidirectional Encoder Representations from Transformers) which is a popular deep learning method recently Many types of research have pointed out that BERT has reached a good achievement in natural language processing Through a great amount of language training data BERT uses a lot of text training to produce a pre-processing model with basic language ability After fine-tuning the pre-processing model generates a summarization model This system converts the video into text and uses the summarization model to generate a text summarization Finally the summarization is rated by subjective comments As a result acceptance and competence reached a good outcome

Date of Award	2020
Original language	English
Supervisor	Yueh-Min Huang (Supervisor)