How GPT Technology Works in Summarizing Tools
GPT stands for Generative Pre-Trained Transformer. Nowadays available in models like GPT 3.5 and GPT 4, this technology is one of the prime ingredients in smart generative tools like ChatGPT, Bard, Ernie, etc.
Other than these types of AI assistants and content generators, GPT is also used in other online tools that deal with content. For example, tools like paraphrasers and article rewriters also utilize it to make their provided results look more natural and human-like.
In the same way, this technology also plays a role in summarizing tools. How it does that, exactly, is what we’re going to look at in this post.
What is GPT Technology?
In simple terms, we can refer to GPT as the technology that allows computer software to create human-like outputs (text, etc.) based on specific user instructions.
And as an illustration, we can cite the example of ChatGPT. When you give it a certain prompt, it gives you a human-like response.
That is what we think, the layman description.
Getting a little specific, we can say that GPT technology is basically a context-perpetuation technology. For example, when we talk about paraphrasing tools, the role of GPT is to help the tool understand the prompt and then provide an output that resonates with it.
It is in this form that GPT features in summarizing tools. It doesn’t generate anything, per se, but it does enable the tool to give back a human-like output to the user.
And while we are on that…
Role of GPT in Summarizing Tools
The role of GPT in summarizing tools is simple. It enables the tools to understand the bigger content and then shorten it in a way that looks like the work of a human writer/editor.
Of course, to get a better idea of this, we should first describe what summarizing tools themselves do in the first place.
Summarizing tools basically have two different types of working. They either summarize the given content in the extractive method or in the abstractive method.
Extractive summarizing is when the summarized form of the text is made up of the same sentences/phrases/passages as the original content. In other words, the original content is merely scoured for the most meaningful parts, which are then compiled together to form the summary.
On the other hand,
Abstractive summarizing is when the text is summarized using different words and phrases than what it originally contains. In other words, the text is read and understood, after which it is written from scratch.
Now, when a summarizing tool creates an extractive summary, all it does is go through the text and find the “heaviest” sentences, i.e., the most meaningful ones, and pile them together.
We want to show you what this looks like, so here is one “extractive” summarizer in action. As you can see, in the output section, the same words and sentences are used as the input field on the right.
But when a tool makes an abstractive summary instead, it rewrites the whole text while shrinking it in size. Here is what that looks like. We’ve used the same text as above, and this time, you can see that the text doesn’t remotely look anything like the input:
Now, coming back to the point…When we talk about the involvement of GPT in summarizing tools, we refer to the ones that work using the abstractive technique. To reiterate, abstractive summarization is about understanding the text and then reproducing it in shortened form. In order to do this, the summarizing tools require GPT.
GPT technology is used in many different AI writing tools. It also plays a major role in summarizers, as we’ve learned in the above post.
The next time you have to use a summarizing tool for shortening your text, you’ll have a good idea of how the backend works.