The document describes VIDSUM, an AI-based web application that uses natural language processing and transformer models to summarize video content in text and audio formats. It aims to address the problem of information overload by extracting meaningful summaries from lengthy videos. The key objectives are to develop VIDSUM using advanced NLP techniques, ensure it has a user-friendly interface, integrate both extractive and abstractive summarization, evaluate its performance compared to other methods, and make it adaptable to different video content types.

1. “ V I D S U M ”
A N I N T E L L I G E N T A I
W E B A P P F O R V I D E O
S U M M A R I Z E R
By: Sangeeta Yadav
TP073558

3. P R O B L E M S T AT E M E N T T R Y I N G T O
R E S O L V E
Information is overload in today’s Digital Era world.
Extracting meaning full summary from videos files are a challenging
task.
Watching lengthy videos are time consuming.
Fulfil the gap of an Efficient Video Content Summarizer

4. O B J E C T I V E S
 Develop VIDSUM Web Application:
Create an AI-based web application named "VIDSUM" for efficient video-to-text summarization.
 Implement NLP and Transformer Models:
Utilize state-of-the-art Natural Language Processing (NLP) techniques and Transformer models for accurate video-to-text conversion within
VIDSUM.
 User-Friendly Interface:
Ensure the VIDSUM user interface is user-friendly and accessible to cater to a broad user base.
 Incorporate Extractive and Abstractive Summarization:
Integrate both extractive and abstractive summarization methods within VIDSUM, providing versatile summarization options for users.
 Performance Evaluation:
Evaluate VIDSUM's performance by comparing the quality and accuracy of its video summaries with traditional summarization methods.
 Adaptability to Various Video Content:
Assess VIDSUM's adaptability to different types of video content, including educational, entertainment, and informational videos.
 Reliable User-Centric Tool:
Provide a reliable and user-centric tool for extracting essential information from video content, saving users' time, and enhancing their
content consumption experience in a fast-paced digital world.

5. S C O P E O F T H E P R O J E C T
Development of VIDSUM:
Create an Intelligent AI
WebApp for Video
Summarizer.
Technology Integration:
Implement advanced NLP and
Transformer models for
precise video-to-text
conversion.
User Accessibility: Design a
user-friendly interface catering
to a broad user base.
Summarization Methods:
Integrate both extractive and
abstractive summarization for
versatile user options.
Performance Evaluation:
Assess VIDSUM's quality and
accuracy compared to
traditional summarization
methods.
Content Adaptability:
Evaluate VIDSUM's
adaptability to various video
types, ensuring versatility.
User-Centric Approach:
Provide a reliable tool for
efficient information extraction,
enhancing content
consumption in the digital era.

6. S O L U T I O N : A
V I D E O / F I L E
S U M M A R I Z E R
"The Intelligent AI Video Summarizer"
App built on Python language using
opensource Video to text model.
It harnesses the Power of Advanced AI
and Machine Learning.
It summarize & transcript any offline
Videos, video links - YouTube to convert
into Text and Audio Formats.
Currently, it supports only English
language.

7. T E C H N O L O G I C A L A P P R O A C H
Using BART : Hugging Face Transformer Model
Implementation in Python
Developed Using Visual Studio Code IDE

8. I N T R O D U C T I O N
T O H U G G I N G
F A C E
T R A N S F O R M E R
M O D E L S
Overview:
• Hugging Face Transformer Models:
Easy-to-use implementation of top-
performing models in Natural
Language Processing (NLP).
Significance:
• State-of-the-Art (SOTA):
Transformers represent the current
SOTA in NLP tasks, including text
classification, text generation,
summarization, and question
answering.

9. B A R T M O D E L B Y H U G G I N G FA C E
BART Model Overview:
Bidirectional and Auto-Regressive Transformers (BART): Introduced in the paper "BART:
Denoising Sequence-to-Sequence Pre-training for NLP."
Features:
Hugging Face Implementation: BART Hugging Face model supports:
• Pre-trained weights
• Fine-tuned weights on tasks like question-answering, text summarization, conditional text generation,
mask filling, and sequence classification.
Application:
Versatile usage across multiple NLP tasks, making it a powerful tool for diverse applications.

10. A R C H I T E C T U R E O F B A R T
Bidirectional and Auto-Regressive Transformers (BART):
• Introduced for NLP tasks in the paper "BART:
Denoising Sequence-to-Sequence Pre-training for
Natural Language Generation, Translation, and
Comprehension."
Core Concepts:
• Bidirectional Transformer:
• Utilizes both left-to-right and right-to-left context,
capturing comprehensive contextual information.
• Auto-Regressive Approach:
• Generates output tokens one at a time, conditioning
each token on the previously generated ones.

11. K E Y C O M P O N E N T S O F B A R T M O D E L
Encoder-Decoder Architecture:
 Encoder:
• Takes input sequence and transforms it into a contextualized representation.
 Decoder:
• Autoregressively generates the output sequence based on the encoder's contextualized representation.
Denoising Objective:
 Training Objective:
• Involves corrupting the input sequence and training the model to reconstruct the original sequence.
Pre-training Tasks:
Text Generation, Summarization, and Translation:
• BART is pre-trained on tasks like text generation, summarization, and translation, enabling it to capture diverse language
patterns.

12. A P P F E AT U R E S
Seamless Integration with YouTube Links
Accurate Video Transcription to Text
Summarization into Audio Format
User-Friendly Interface for Customization

13. R E A L U S E
C A S E S
• Efficient Learning and Educational
Resource Management
• Rapid Business Insights and Market
Analysis
• Accessibility for Visually Impaired
Users
• Accelerated Content Review and
Decision Making

14. B E N E F I T S A N D
I M PA C T
 Time-Saving: Quick Access to Key
Content
 Enhanced Productivity: Effective
Information Consumption
 Accessibility: Catering to a Diverse
Audience
 Advancing AI Application in Practical
Scenarios

15. F U T U R E P R O S P E C T S
Continuous Enhancement
based on User Feedback
Expansion to Other
Languages and Accents
Collaboration with
Learning Platforms
AI Advancements for
Even More Precise
Summarization

16. Thank You!!