This PPT about the video to text summarization

1. VIDEO TO TEXT SUMMARIZER
USING AI
DONE BY
DHINAGARAN P
GOKULNATH G
PERINBAN M
SUDHARSAN M
PROJECT GUIDE
DR . S V MANISEKARAN
ASSISTANT PROFESSOR
DEPARTMENT OF INFORMATION TECHNOLOGY
ANNA UNIVERSITY REGIONAL CAMPUS
COIMBATORE

2. ABSTRACT
● Video to Text Summarizer is the process of converting a large video to text. And
this has been summarized in the smaller one. It helps for various time consuming
process to save the time.
● In this mini project, the various Techniques and Algorithms have been used for the
summarization. You will get exact summarization of a particular video and this will
help to learn more things in easy way and understandable manner.
● The video-to-text Summarizer provides numerous applications including efficient
content browsing, video indexing, and information retrieval. Users can quickly
obtain an overview of video content, enabling them to make informed decisions
about further engagement or to locate specific information within a large video
dataset.

3. OVERALL DESIGN

4. ALGORITHM - TextRank
● Textrank is a graph-based ranking algorithm like Google’s Pagerank
algorithm which has been successfully implemented in citation analysis.
● It is used for keyword extraction , automated text summarization and
phrase ranking.
● The textrank algorithm ( keyword extraction / sentence ranking)
constructs a word network by looking which words follow one another
and setting a link between two words if they follow one another, the link
getting a higher weight if these two words occur frequently next to each
other.

5. SYSTEM IMPLEMENTATION
Data Acquisition Module: This module handles the acquisition of video data from various
sources, such as YouTube or local video files. It may include functionalities for downloading videos,
extracting audio, and handling different video formats.
Speech Recognition Module: This module is responsible for converting the audio content of
the videos into text. It utilizes speech recognition algorithms or services to transcribe the spoken
words into textual form.
Text Processing Module: This module processes the transcribed text to extract key information. It
may include functionalities such as text cleaning, tokenization, part-of speech tagging, named
entity recognition, and syntactic parsing.
Summarization Module: This module generates concise summaries based on the processed
text. It utilizes summarization algorithms, which could be extractive (selecting important
sentences or phrases) or abstractive (generating new sentences) in nature

6. TOOLS REQUIRED
Python: The code is written in Python, a widely used programming language known for
its simplicity and versatility.
Speech Recognition library: The speech recognition library in Python provides an
interface to access multiple speech recognition APIs and services.
Hugging Face's Transformers library: The transformers library, specifically the
pipeline module, is part of the Hugging Face's Transformers library. It enables easy usage
of pre-trained models for natural language processing tasks, such as summarization.

7. EXPERIMENTAL RESULTS

8. ADVANTAGES
Efficient information extraction: Video-to-text summarizers can quickly analyze and
extract key information from videos, saving users time and effort.
Easy navigation and referencing: Once a video is converted into text, the summarized
version can be easily scanned and searched for specific keywords or sections of interest.
Accessibility and inclusivity: Text-based summaries make video content more
accessible to individuals with hearing impairments or those who prefer reading over
watching videos

9. DISADVANTAGES
Quality of the video source: The quality of the video source can impact the accuracy
and effectiveness of video-to-text summarization. Videos with poor audio quality, low
resolution, or unclear visuals may result in less accurate transcriptions and subsequently
affect the quality of the text summary.
Internet Connectivity: During the process of summarization there should be a good
internet connection is must otherwise the process take time.
Video Duration: The Video Duration is also a important factor if the duration of the
summarizing video is larger it takes more time to summarize

10. REFERENCES
1. "Transcribing Video Content to Text" by Haowen Xu, Shih-Fu Chang, and Tat-Seng
Chua. (2019) - This paper introduces a method for transcribing video content to text using
a multimodal approach, combining visual and auditory cues.
2. "Video Summarization Using Deep Semantic Features" by Luming Tang, Xinxiao Wu,
and Wenjing Jia. (2018) - The authors propose a video summarization technique that
utilizes deep semantic features to extract key information from videos and generate
concise summaries.
3. "Unsupervised Video Summarization with Adversarial LSTM Networks" by Mahnaz
Koupaee and William Yang Wang. (2018) - This paper presents an unsupervised video
summarization method that employs adversarial LSTM networks to generate summaries
by selecting representative frames.

11. REFERENCES
4. "Video Summarization by Learning from Unpaired Data" by Debidatta Dwibedi, Yusuf
Aytar, Jonathan Tompson, Pierre Sermanet, and Andrew Zisserman. (2018) - The authors
propose a video summarization approach that learns from unpaired videos and their
corresponding titles, leveraging a combination of deep reinforcement learning and
unsupervised learning.
5. "Deep Reinforcement Learning for Unsupervised Video Summarization with
Diversity-Representativeness Reward" by Yu Gong, Qiang Zhang, and Ming-Hsuan
Yang. (2017) - This paper introduces a deep reinforcement learning framework for
unsupervised video summarization

12. THANK YOU