How to Summarize Many YouTube Videos at Once

 

I created an automation that streamlines the process of gathering and analyzing YouTube video content. This tool efficiently compiles summaries from multiple videos, highlighting common themes, differing viewpoints, and useful product recommendations.

By leveraging services like Apify for transcription and OpenAI for summarization, I can extract valuable insights without manually sifting through each video.

Key Components of the Automation

• Google Sheets: This acts as the primary interface where I input the video links and topics for analysis.
• Apify: A reliable service to fetch YouTube transcripts, which provides accurate text for summarization.
• OpenAI: It processes the transcripts to generate concise summaries, focusing on actionable insights.
• Document Creation: The automation formats the summaries into a structured document, making it easy to review and share.

Curating Video List

To kick off the analysis, I prefer to manually curate a list of relevant YouTube videos. This approach ensures that I select high-quality content that aligns with my research goals. For instance, I might search for topics like “best SEO tools” and handpick videos based on their credibility and relevance.

Steps to Curate Videos

1. Conduct a search on YouTube for a specific topic.
2. Review the search results and identify videos that appear credible or have high engagement.
3. Copy the links of the selected videos into a notepad or directly into Google Sheets.
4. Label the topic for future reference and ensure the document title reflects the content.

Setting Up a Make Scenario

Once the video list is ready, I set up a scenario in Make.com to automate the data processing. This scenario watches for new entries in the Google Sheet, triggering the automation each time I add a new video link.

Creating the Scenario

Here’s how I establish the scenario:

• Connect to Google Sheets and set it to watch for new rows in the designated spreadsheet.
• Use the iterator tool to process each video link, splitting the data as necessary.
• Create a text aggregator to combine the video links into a single text block for easier processing.

Fetching YouTube Transcripts

Fetching transcripts is a crucial step. I use Apify to obtain accurate transcripts from the selected YouTube videos. This service is efficient and provides reliable results, allowing me to focus on analyzing the content rather than transcribing it manually.

Steps to Fetch Transcripts

1. Create an Apify account and retrieve your API key.
2. Add an Apify module in Make.com and select the appropriate actor for YouTube transcripts.
3. Input the video URLs into the Apify module and execute the actor to obtain the transcripts.
4. Store the transcript data in a structured format for easy access during the summarization process.

Using AI to Summarize Videos

I created an automation that leverages AI to summarize various YouTube videos efficiently. The process begins by fetching transcripts from the videos using Apify. Once I have the transcripts, I send them to OpenAI for summarization. This approach allows me to condense the information into actionable insights without manually watching each video.

How the Summarization Works

When the transcripts are ready, I input them into OpenAI with a clear instruction to focus on actionable insights, tips, and any software or tools mentioned in the videos. This ensures that the summary is not only concise but also relevant to my research objectives.

OpenAI processes the transcripts and returns a summary that captures the essence of each video. This summary includes key points, differing opinions, and product recommendations. The result is a well-rounded overview that highlights the most important aspects of the videos.

Aggregating Results

After obtaining individual summaries, I aggregate the results into one cohesive document. This is done by using another OpenAI module that compiles all the summaries into a single output. Aggregation is crucial as it allows me to see common themes and contrasting views across the videos.

Steps for Aggregation

1. Gather all individual video summaries from OpenAI.
2. Create a text aggregator in Make.com to combine these summaries.
3. Format the aggregated text for clarity, ensuring it is easy to read and understand.

In this phase, I also include links to the original videos for reference. This way, if I want to dive deeper into any specific video later, I can easily access it. The aggregated summary serves as a quick reference guide for my research.

Creating a Google Doc

Once I have the aggregated results, the next step is to create a Google Doc that contains all the summaries and insights. This document serves as a central hub for my research, allowing for easy access and sharing.

Process of Document Creation

1. Set up a Google Docs module in Make.com.
2. Name the document based on the topic of research.
3. Input the aggregated summaries into the document content.

I ensure that the formatting in the Google Doc is clean and structured. This makes it easy to read and navigate through the insights. Adding headings and bullet points helps in organizing the information logically.

AI for Video Comparison

Beyond summarization, I also use AI to compare the videos. This involves analyzing the aggregated summaries to identify common themes and differing viewpoints. By doing this, I can understand the landscape of opinions on a given topic.

Using NotebookLM as an Alternative

If you’re looking for a more ad-hoc analysis then NotebookLM is an absolutely fantastic option. This tool allows you to input video links directly and receive summaries without the need for creating an automation. It’s particularly useful for quick, ad hoc analysis as well as having back and forth conversion with minimal hallucinations.

How to Use NotebookLM

1. Access NotebookLM and create a new project.
2. Copy and paste the YouTube links of the videos you want to analyze.
3. Prompt NotebookLM to summarize the content and highlight key insights.
4. Review the generated report for common themes and differing viewpoints.

NotebookLM is particularly effective for those who need quick insights without setting up an entire automation. While it requires manual input, the results are often just as valuable, providing a clear overview of the content analyzed.

Benefits of the Automated Approach

The automated approach offers numerous advantages. It significantly reduces the time spent on gathering and analyzing video content. Additionally, it can handle larger volumes of data, making it easier to conduct comprehensive research. However, NotebookLM is also a fantastic option, so I generally use both approaches based on the context.