Open AccessSurvey on Abstractive Transcript Summarization of YouTube Videos
Author(s) -
S. Dinesh Tharun,
R. Kranthi Kumar,
P. Sai Sravanth,
G. Srujan Reddy,
B. Akshay
Publication year - 2022
Publication title -
international journal of advanced research in science, communication and technology
Language(s) - English
Resource type - Journals
ISSN - 2581-9429
DOI - 10.48175/ijarsct-3181
Subject(s) - automatic summarization , computer science , selection (genetic algorithm) , task (project management) , reading (process) , information retrieval , the internet , natural language processing , artificial intelligence , world wide web , linguistics , philosophy , management , economics
Thousands of video recordings are created and shared on the internet every day. It is becoming increasingly difficult to spend time to watch such videos, which may take longer than anticipated, and our efforts may go in vain if we are unable to extract meaningful information from them. Summarizing transcripts of such videos helps us to quickly search for relevant patterns in the video without having to go through the entire content. Abstractive transcript summarization model is very useful in extracting YouTube video transcripts and generates a summarized version. An automatic summarizer's purpose is to shorten the time of reading, enable easier selection, be less prejudiced compared to humans, and portray content that is compressed while preserving the important material of the actual document. Extractive and abstractive approaches are the two most common ways to summarise text. Extractive approaches choose phrases or sentences from input text, whereas Abstractive methods generate new words from input text, making the task much more difficult.