Deep Tech Point
first stop in your tech adventure

Summarizing Through Prompting: Extracting And Summarizing Information With ChatGPT

April 20, 2024 | AI

In the realm of human-computer interaction, the process of prompting involves engaging with users to extract information or guide conversations towards desired outcomes. Within this framework, summarization plays a crucial role in distilling and presenting key information in a clear and concise manner. Whether it’s clarifying complex discussions, aiding decision-making, or providing memory aids, summarization enhances the efficiency and effectiveness of conversational systems. This article explores the various techniques used for generating summaries in prompting systems and their applications in improving user experiences and interactions.

When prompting, summarization refers to the process of condensing or distilling information provided by the user or generated by the system into a concise and coherent form. It involves extracting the most relevant and important points from a conversation or interaction and presenting them in a structured manner.

In the context of conversational AI or human-computer interaction, summarization can serve various purposes:

Overall, summarization in prompting enhances the effectiveness and usability of conversational systems by presenting information in a clear, concise, and easily digestible format. It enables users to extract value from interactions more efficiently and facilitates better decision-making and understanding.

What is the difference between summarizing and extracting information with ChatGPT?

Summarizing and extracting information with ChatGPT involve distinct processes and serve different purposes. In essence, summarizing aims to provide a condensed overview of the input text, capturing its main ideas, while extracting information aims to retrieve specific details or answers from the text. Both processes can be valuable in different contexts, depending on the user’s information needs and the nature of the task at hand. Let’s take a look:

Techniques used for generating summaries in prompting systems

Several techniques are used for generating summaries in prompting systems, each with its strengths and weaknesses. Here are some common techniques:

Extractive Summarization

Abstractive Summarization

Hybrid Approaches

Query-focused Summarization

Compression Techniques

Now, let’s take an example of the following text:

“The demand for renewable energy sources, such as solar and wind power, has been steadily increasing in recent years due to growing concerns about climate change and the finite nature of fossil fuels. Solar energy, harnessed from sunlight using photovoltaic cells, offers a clean and abundant source of power. Similarly, wind energy, generated by wind turbines, is becoming increasingly popular as advancements in technology improve efficiency and reduce costs. These renewable energy sources not only reduce greenhouse gas emissions but also promote energy independence and sustainability.”

In the following section we will provide examples for each of the summaries mentioned above. Each of these examples demonstrates a different approach to summarizing the original text, highlighting the diversity of techniques available for generating summaries in prompting systems.

Extractive summary:

The demand for renewable energy is rising due to climate change concerns and finite fossil fuels. Solar and wind power are popular sources, offering clean and abundant energy.

Abstractive summary

The increasing demand for renewable energy, driven by climate change concerns and finite fossil fuel reserves, has led to the growing popularity of solar and wind power. Solar energy, harvested through photovoltaic cells, provides a sustainable and abundant energy source. Wind energy, generated by turbines, is gaining traction with advancing technology, offering clean and efficient power solutions.

Hybrid summary

The demand for renewable energy sources like solar and wind power is on the rise due to concerns about climate change and finite fossil fuels. Solar energy, derived from sunlight via photovoltaic cells, and wind energy, harnessed by turbines, are becoming increasingly popular for their sustainability and clean energy benefits.

Query-focused Summarization:

Query: “What are the benefits of solar and wind power?”
Query-focused summary: “Solar and wind power offer clean and sustainable energy solutions, addressing concerns about climate change and finite fossil fuel reserves.”
Compression Techniques:

Compressed summary

Rising demand for renewable energy, like solar and wind power, driven by climate change concerns and finite fossil fuels. These sources offer clean, abundant, and sustainable energy solutions.

A Few Examples of Summarizing Tasks for ChatGPT

Summarizing text with a word, sentence or even character limit

Sometimes this task can be a bit daunting because large language models measure the length they produce in tokens (1 token = approximately 4 character) and not in words or sentences or even characters. However, they are getting close to understanding the concept of a word, sentence and a character limit.

Example of a prompt in which we would like GPT to summarize text with a word, sentence or even character limit:

Your task is to generate a short summary of a product from an ecommerce site. Summarize the review below, delimited by double square brackets, in at most 50 words:
[[text about a product from an ecommerce site]]

Example of a prompt, where we want GPT to focus on specific area of interest

For example, we want the GPT to summarize a product review, but only those that have a focus on shipping and delivery. Why? Well, maybe because we want to provide feedback to the shipping department.

“Summarize the review below, delimited by double square brackets, in at most 100 words. Focus on any aspects that mention shipping and delivery of the product: [[text]]”

Summarize with a focus on specific area of interest

“Summarize the review below, delimited by double square brackets, in at most 30 words, and focus on any aspects that are relevant to the price and perceived value:[[text]]”

In conclusion

Summarization in prompting systems is a multifaceted process that empowers users to navigate complex information and interactions more effectively. By condensing and presenting key points in a structured format, summarization facilitates clearer communication, aids decision-making, and enhances user understanding and memory retention. From extractive and abstractive techniques to hybrid approaches and query-focused summarization, a diverse range of methods exists to cater to different user needs and contexts. As prompting systems continue to evolve, the integration of advanced summarization techniques will play a crucial role in shaping more intuitive and efficient human-computer interactions.