How to Build a YouTube Video Summarizer Using LLMs

Large Language Models (LLMs) have emerged as a game-changer in artificial intelligence, capable of generating and comprehending text. Their applications span various domains, from machine translation to creative writing. One intriguing use case for LLMs is YouTube video summarization — a tool that can condense lengthy videos into concise summaries. In this guide, we’ll explore how to build a YouTube video summarizer using LLMs, unlocking the potential to save time and enhance content accessibility.

1. Step 1 - Collect a Dataset of YouTube Videos and Summaries: The first step in building a YouTube video summarizer is to assemble a dataset of YouTube videos and their corresponding summaries. These summaries can be manually created or obtained using web scraping tools. The dataset should encompass a diverse range of video types, including news, education, and entertainment. The summaries should be clear and succinct, effectively capturing the main points of each video.
2. Step 2 - Train an LLM on the Dataset: With your dataset in hand, it’s time to train an LLM. Numerous LLM training frameworks, such as TensorFlow or PyTorch, can be used for this purpose. Ensure that the dataset is representative of the YouTube videos you intend to summarize. Training the LLM sufficiently is crucial to enable it to generate accurate and informative summaries.
3. Step 3 - Develop a Summarization Algorithm: The core of your YouTube video summarizer is the summarization algorithm. This algorithm utilizes the trained LLM to generate summaries for new YouTube videos. The algorithm can be as straightforward as generating a summary based on the video’s title and description or more complex, involving the generation of a summary from the video’s transcript. If you opt for transcript-based summarization, you’ll need a speech-to-text transcription service to convert the video’s audio into text.

Here is a sample code snippet to help you get started with a YouTube video summarization app using LLMs:

import transformers
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer

# Load the pre-trained LLM
model = AutoModelForSeq2SeqLM.from_pretrained("google/bigbird-roberta-base")
tokenizer = AutoTokenizer.from_pretrained("google/bigbird-roberta-base")

# Summarize YouTube video function
def summarize_video(video_url):
    # Get the video's transcript
    transcript = get_video_transcript(video_url)
    # Generate a summary of the transcript using the LLM
    summary = model.generate(
        input_ids=tokenizer(transcript, return_tensors="pt").input_ids,
        max_length=128,
        num_beams=5,
    )[0]
    # Decode the summary
    summary = tokenizer.decode(summary, skip_special_tokens=True)
    return summary


# Example usage:
video_url = "https://www.youtube.com/watch?v=dQw4w9WgXcQ"
summary = summarize_video(video_url)
print(summary)

Potential Time Savings:

You can calculate the potential time savings a YouTube video summarizer can offer using the formula:

Savings in time = Length of video — Length of summary

For instance, if a video is 1 hour long and the summarizer generates a summary in 2 minutes, and since reading speed is much higher in general, it saves the user > 30 minutes of time per a single video that is 1 hour long.

Challenges to Consider: Building a YouTube video summarizer using LLMs comes with challenges:

1. LLM Training: Collecting and labeling a substantial dataset for LLM training can be time-consuming.
2. Accuracy: Generating accurate summaries for long or complex videos can be challenging.
3. Multi-Modal Data: YouTube videos contain audio and video data, making it complex to develop an LLM that can process and generate text, images, and other data types.

Potential Savings: The potential time savings with a YouTube video summarizer are substantial. Users can quickly grasp the essence of a video without watching the entire content. For educational purposes, creating searchable video transcripts becomes feasible, enhancing content accessibility.

In conclusion, building a YouTube video summarizer using LLMs is an exciting endeavor with practical applications. As you embark on this journey, consider the dataset, LLM training, and the summarization algorithm carefully. With time-saving benefits and improved content accessibility, your YouTube video summarizer could prove to be a valuable tool in the digital age.