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You will learn to:
Get transcripts of YouTube videos using Python.
Build a Python script to interact with human language data.
Build the process of tokenizing or splitting a string/text into a list of tokens.
Generate a summary of the text using the Natural Language Toolkit.
Skills
Machine Learning
Natural Language Processing
Tokenization
Prerequisites
Intermediate knowledge of Python.
Intermediate knowledge of Natural Language Processing.
Basic understanding of spaCy models.
Intermediate knowledge of sentence tokenizer.
Technologies
NLTK
spaCy
Python
Project Description
In this project, we’ll develop a YouTube video transcript summarizer that automatically extracts video transcripts from YouTube and generates concise summaries using the Natural Language Toolkit (NLTK) and sentence tokenization techniques.
To accomplish this, we’ll utilize the YouTube API to fetch the video based on the provided URL or video ID. Once the video is obtained, we’ll get the text of the video using the transcript.
With the video transcript in hand, we’ll leverage the powerful features of NLTK, a widely used natural language processing library, to tokenize the transcript into individual sentences. This sentence tokenization step allows us to break down the transcript into smaller units for analysis.
To generate the summary, we’ll utilize NLTK’s summarization algorithms. By applying techniques like extractive summarization, we’ll identify the most significant sentences and construct a condensed summary that captures the key points and main ideas of the video.
Project Tasks
1
Video-to-Text Conversion
Task 0: Get Started
Task 1: Import Modules
Task 2: Get the ID of the YouTube Video
Task 3: Get a Transcript of Video
2
Text to Summary Conversion
Task 4: Get All Available Sentences
Task 5: Get All Tokens from the Document
Task 6: Calculate the Frequency of Tokens
Task 7: Normalize the Frequency of Tokens
Task 8: Calculate the Score of Sentences
Task 9: Generate the Summary
Congratulations!