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Harnessing ChatGPT for Efficient Summarization Techniques

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Chapter 1: Introduction to Summarization Agents

Are you someone who leaves reviews on Google Maps each time you dine at a new restaurant? Or do you find yourself sharing your thoughts on Amazon purchases, especially when a product disappoints? No judgment here—we all have our moments!

In today’s data-driven world, we contribute to an overwhelming influx of information in various forms. One particularly fascinating data type is textual data, such as the myriad of reviews posted online daily. Have you ever considered the significance of organizing and condensing this information?

Welcome to the realm of summarization agents! These agents, akin to the AI image generation tool DALL-E, play a crucial role in our daily lives by distilling information and granting quick access to relevant content across numerous platforms.

In this article, we will delve into the use of ChatGPT as a robust summarization tool for custom applications. Thanks to Large Language Models (LLMs) that can comprehend and interpret texts, they assist in reading and producing concise summaries or standardizing data. However, it’s essential to understand how to harness their capabilities effectively while recognizing their limitations.

Section 1.1: Limitations of Summarization

What’s the primary challenge with summarization? LLMs frequently struggle to meet specific character or word count requirements in their summaries.

Let’s examine best practices for creating summaries with ChatGPT in our custom applications, as well as the reasons behind its limitations and strategies to overcome them!

Chapter 2: The Role of Summarization Agents

Summarization agents are ubiquitous on the Internet. For instance, websites leverage these tools to provide succinct summaries of articles, allowing users to quickly grasp the news without reading the entire piece. Social media platforms and search engines utilize them as well.

From news aggregators to e-commerce websites, summarization agents have become integral to our digital experience. With the rise of LLMs, many of these agents now employ AI for enhanced summarization outcomes.

ChatGPT can be a valuable partner when developing an application that employs summarization agents to accelerate reading tasks and categorize texts. For example, in an e-commerce context, ChatGPT could assist in summarizing customer reviews into a few sentences, standardizing the format, assessing sentiment, and classifying the feedback accordingly.

While we could simply input the review into ChatGPT, there are several best practices—and pitfalls—to consider in order to maximize its potential for this specific task.

Section 2.1: Practical Example: E-commerce Reviews

Let’s bring this concept to life with an example! Imagine we want to analyze all reviews for a popular product on our e-commerce platform, such as a beginner computer for children. In this scenario, we want ChatGPT to:

  1. Classify the review as positive or negative.
  2. Summarize the review in 20 words.
  3. Format the response in a consistent structure.

Here’s a foundational code structure for prompting ChatGPT from our custom application. Additionally, I’ll link to a Jupyter Notebook containing all the examples discussed in this article.

The function get_completion() calls the ChatGPT API with a specific prompt. If there’s additional user text, such as the review itself, it is separated from the main code using triple quotes.

Let's use the get_completion() function to prompt ChatGPT!

Section 2.2: Refining the Prompt

⚠️ The prompting guidelines applied in this example, such as using delimiters to distinguish input text from the rest of the prompt and requesting structured output, are thoroughly explained in "What I Learned from OpenAI's Course on Prompt Engineering—Prompting Guidelines."

Here’s ChatGPT’s output based on our initial prompt:

As we can see from the output, the review is accurate and well-organized. However, it lacks some critical details that might interest us as e-commerce owners, such as shipping information.

To enhance the summary, let’s focus on Shipping and Delivery specifics:

Now, the revised summary includes more comprehensive details. Highlighting essential aspects of the original review is vital to ensure ChatGPT does not overlook valuable information.

Section 2.3: Moving from Summarization to Extraction

Interestingly, I discovered that summarization can be challenging for LLMs if user prompts lack precision. When requesting a summary, ChatGPT might overlook relevant details—just as we experienced—or assign equal importance to all topics, merely providing a general overview.

Experts often prefer the term extraction when working with LLMs for such tasks. Instead of summarizing, extraction aims to retrieve specific details, giving us exactly what we need.

Let’s try this approach!

By implementing extraction, we can focus solely on our topic of interest: Shipping: Arrived a day earlier than expected.

Section 2.4: Automating the Process

This system functions for a single review. However, when designing prompts for applications, testing them across multiple examples is crucial to identify any anomalies or unexpected behavior in the model.

If processing numerous reviews, here’s a sample Python code structure that could assist in summarizing our batch of reviews.

⚠️ Note that, despite our clear word restrictions in the prompts, we observe that none of the iterations adhered to the specified word limits. This discrepancy arises because LLMs lack precise comprehension of word or character counts.

The reason for this lies in a fundamental component of their architecture: the tokenizer.

Section 2.5: Understanding the Tokenizer

LLMs like ChatGPT are engineered to generate text based on statistical patterns acquired from extensive language datasets. While they excel at producing coherent and fluent text, they do not have precise control over word counts.

As seen in previous examples, ChatGPT struggles to adhere to strict word limits. It may generate text that is shorter or longer than intended, prioritizing coherence and relevance over strict compliance with word counts.

The tokenizer is the key element in ChatGPT's architecture that significantly influences the length of the generated output.

The tokenizer initiates the text generation process by breaking down the input text into tokens, which the language model then processes to generate new text. However, the tokenizer's rules are not infallible, and it may split or merge tokens in ways that impact the total word count.

For instance, consider the phrase: "I want to eat a peanut butter sandwich." Depending on how the tokenizer is set up, it might break this down into different token counts, affecting the overall word count.

Being aware of these limitations can assist you in crafting suitable prompts for your specific application.

Wrapping up: Final Thoughts on E-commerce Reviews

Let’s consolidate our insights from this article into a final prompt! In this case, we will request results in HTML format for a more polished output:

Here’s the final output from ChatGPT based on our refined prompt:

In this article, we explored best practices for utilizing ChatGPT as a summarization agent in custom applications. We learned that crafting the perfect prompt for application requirements often takes several iterations.

A key takeaway is to view prompting as an iterative process, refining your approach until you achieve the desired results.

By continually adjusting your prompt and testing it against a batch of examples before deploying it, you can ensure consistent output across various inputs and address outlier responses.

Furthermore, understanding the strengths and weaknesses of LLMs in your specific context is essential. This leads to the realization that extraction tasks often yield better results than traditional summarization, particularly when focused on specific details.

Finally, while LLMs are powerful tools for text generation, they may not reliably follow precise word count or formatting guidelines. To meet these requirements, you might need to consider alternative methods, such as sentence counting or manual editing.

Thank you for reading! I hope this article aids you in building custom applications with ChatGPT.

Feel free to subscribe to my newsletter for updates on new content, especially if you're interested in ChatGPT-related articles. Should you have any questions, do not hesitate to reach out to [email protected]!

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