Exploring text summarization using LLM tools

Nov 13, 2023 ·

Text summarization is an important tool to quickly comprehend large amounts of information. It can be used for various purposes such as getting a quick overview of a research paper, understanding the main ideas of a long article, delivering news stories in a concise format or helping to generate summaries for reports, etc.

The purpose of this article is to explore text summarization using Long Language Model (LLM) tools. The main idea is to use a two-pronged approach: using natural language processing (NLP) to capture the key content and then using LLMs to determine the most salient points in the text.

Using NLP techniques, we can identify words and phrases that are important, while also ignoring those that are not. The output of this step will be a set of keywords that form the framework of the summary. We can then use LLMs to distill the text into the most relevant sentences.

To illustrate, let's take a look at an example. Suppose we have the following text: "Although it might seem easy to write a summary of this text, it is actually quite a challenge to accurately capture the main ideas without going into too much detail."

Using NLP techniques, the important words or phrases can be identified as follows: summary, challenge, main ideas, detail. Using LLMs, the summary can be distilled to focus on the most relevant sentences, such as: "Writing a summary of this text is a challenging task, as it requires accurately capturing the main ideas without going into too much detail."

In conclusion, text summarization is an important tool for quickly understanding information. By utilizing NLP techniques to identify keywords and LLMs to determine the most salient points, we can craft accurate and concise summaries. This article has demonstrated how this two-pronged approach can be used to effectively summarize text.