So what are these transformer things? Well, they’re basically fancy neural networks that can learn how to process language in a different way than traditional models. Instead of breaking down sentences into individual words like a human would do (which is called tokenization), the transformers work by looking at all the words at once and figuring out which ones are most important for understanding the meaning of the sentence as a whole.

Here’s an example to help illustrate how this works: let’s say we have the following text document: “The quick brown fox jumps over the lazy dog.” If we were using traditional tokenization methods, our model would break down each word into its own individual piece and then try to figure out what they all mean separately. But with transformers, instead of doing that, it looks at the entire sentence as a whole and tries to understand how all the words work together to create meaning.

So why is this important? Well, for one thing, it’s much more efficient than traditional methods because we don’t have to process each word separately. And since transformers can handle longer sentences without breaking them down into smaller pieces, they’re better at understanding the context of a sentence and figuring out what it means as a whole.

But that’s not all! Another cool thing about this paper is that instead of using all the words in a document to extract information (which can be computationally expensive), the authors use only a select few “sparse” tokens to make their model more efficient and less resource-intensive. This means that they can process larger documents without slowing down or running out of memory, which is especially important for applications like text mining and document classification where we need to analyze large amounts of data quickly and accurately.

In simpler terms, this paper shows us how to use transformers to extract information from text documents using only a select few “sparse” tokens, making the process more efficient and less computationally expensive than traditional methods. And since transformers are better at understanding context and figuring out what sentences mean as a whole, they’re great for applications like text mining and document classification where we need to analyze large amounts of data quickly and accurately.