Creating a Text and PDF Summaries in 1 Minute | AI Tools for Small Business and Creators

Creating a Text and PDF Summarizer in 1 Minute | AI Tools for Small Business and Creators

Hey everybody, this is Joe TheI, and today I'll be covering how to build your own Tech summarizer using AI. Once you finish this project, it can be expanded to include audio, video, and PDFs. This article summarizes my live stream, which was hosted on YouTube and Twitch. You can find those links here as well as in the descriptions below.

I'll cover the progress we made during the live stream and then discuss potential use case extensions and where you could take this project from here. So let's get started.

The great thing about this use case is that it runs on a free and basic Google Colab notebook. Your laptop or computer might have more resources available, which can make this run even faster. We'll be using Facebook's BART model, which is available in the Transformers package in Python. With these tools, you can build a summarizer in about 10 lines of code; it's really that simple.

First, let's set up our environment. Google Colab is a free cloud service that supports Python and is perfect for this kind of project. Here's how you can install the necessary package:

1. Open up a new Google Colab notebook.
2. Add a code cell and run the following command:

   !pip install transformers
   

Once the installation is complete, you're now ready to start coding. We'll import the BART model, the tokenizer, and other necessary components from the Transformers package. Next, we need to set up the model and the tokenizer in the code, which can be done in just two lines.

Here's what you need to do next:

1. Use the tokenizer to encode your text.
2. Generate the summary from the encoded text.
3. Decode the summary back to plain text.

With this code, you can summarize any piece of text out there. For instance, you can use it to summarize articles, reports, or any large block of text.

During the live stream, I demonstrated how you could add PDF input to the summarizer instead of just text. I used the story "A Tale of Two Cities," which is in the public domain. However, you can use any PDF that you want. Personally, I encountered some issues with PDFs because it was a digital scan of the book. The reader struggled with the spacing between the characters, hyphens, and semicolons, which led to poor output from the summarizer. As the saying goes in data science: garbage in is garbage out. Always remember that the quality of your input data is crucial.

From here, there are several different ways you could extend the functionality of this summarizer. First, you could set up web scraping to pull articles or data from websites and summarize those. You could work with APIs to integrate directly with them and fetch content dynamically, then use it for summarization. You could do speech-to-text to convert speech like what you hear in this video and then summarize that transcribed text. You could also convert documents and insert images into the text and then summarize those. These extensions can significantly enhance the versatility of your summarizer.

In the second half of the stream, I worked on improving the summarization method by switching from text splitting to text chunking. The difference here is that chunking creates overlap in the text pieces, which helps to preserve context better than simply splitting text at whatever character limits are set. The trade-off is that this increases the amount of data to be processed, which can lead to longer processing times if your computer or laptop lacks power.

Overall, it was amazing how quickly you can build powerful tools like this and tailor them to your specific needs. With a basic understanding of Python and the Transformer Library, we created a summarizer in no time. Tools like these can help you curate your data sets, analyze large volumes of text, and extract meaningful insights quickly.

If you've made it this far, thanks for reading! I've included a link for the Colab notebook in the comments for you to use. Just make a copy, and you'll be able to run it all on your own. If you found this article helpful, don't forget to like, comment, and subscribe for more tech and AI insights. If you have any questions or suggestions for future projects or topics you'd like covered, drop a comment below or check out my other videos. Thank you, and I'll see you next time.

Keywords

• AI summarizer
• Google Colab
• Python
• Transformers package
• Facebook BART model
• Text summarization
• PDF summarization
• Data science
• Web scraping
• APIs
• Speech-to-text
• Text chunking

FAQ

Q: What is the BART model and why is it used? A: The BART model is a Transformer-based model from Facebook that's particularly well-suited for tasks like text summarization. It's integrated with the Transformers package in Python, making it easy to use.

Q: How can I set up my environment to build a summarizer? A: You can use Google Colab, a free cloud service that supports Python. Install the necessary package by running !pip install transformers in a code cell within Colab.

Q: What are the main steps to implement text summarization? A: You need to use the tokenizer to encode your text, generate the summary from the encoded text, and then decode the summary back to plain text.

Q: Can this summarizer handle PDFs? A: Yes, but be cautious. If the PDF is a digital scan with poor formatting, the summarizer may produce poor output. Ensure your input data is of high quality.

Q: How can I extend the summarizer's functionality? A: You can set up web scraping to pull articles, work with APIs to fetch content dynamically, convert speech to text, and even handle documents with images.

Q: What's the difference between text splitting and text chunking? A: Text chunking creates overlaps in text pieces to preserve context better, whereas text splitting may arbitrarily cut off text at character limits. However, chunking increases the amount of data to be processed and can lead to longer processing times.