Understanding the AI Building Blocks: How Gemini Summarizes and Classifies Text

The research and resources we just reviewed provide a fantastic high-level view of how language models are changing the landscape of work. But theory only takes us so far. To build practical workflows in Google Workspace Studio, we need to move from the abstract 'what' to the concrete 'how' by looking at the specific AI engine we'll be using.

Let's get specific. At the heart of our AI-powered automations are two fundamental, almost magical, capabilities: the ability to distill vast amounts of text into a concise summary and the power to instantly categorize that text into meaningful buckets. Think of your overflowing inbox. How many hours could you save if an assistant could pre-read every email, hand you a one-sentence summary, and tag it as 'Urgent,' 'Action Required,' or 'FYI'?

This is the core problem that Large Language Models (LLMs) like Google's Gemini are designed to solve. In this section, we’ll pull back the curtain and understand the basic principles of how Gemini performs these two critical tasks: summarization and classification. Grasping these concepts is the key to unlocking powerful automations for your daily work.

First, let's talk about summarization. At its simplest, it's the process of creating a shorter version of a text that contains its most important points. Early summarization techniques were often extractive—they would identify and pull out key sentences from the original document. While useful, the results could feel disjointed.

Modern models like Gemini, however, excel at abstractive summarization. This is a far more sophisticated approach. Instead of just copying sentences, the AI reads and understands the source text, then generates brand-new sentences to articulate the core message. It’s the difference between highlighting a book and writing a clear, concise review of it. This ability to rephrase and synthesize is what makes AI-generated summaries so natural and effective for our workflows.

The second building block is text classification, also known as categorization. Imagine you have a set of predefined labels or 'buckets.' Classification is the task of assigning the most appropriate label to a piece of text. For our workflows, this is incredibly powerful.

This could be simple, like sorting incoming support tickets into folders: 'Billing,' 'Technical Issue,' 'Feature Request.' Or it could be more nuanced, like performing sentiment analysis on customer feedback, automatically tagging it as 'Positive,' 'Negative,' or 'Neutral.' By transforming unstructured text into structured, labeled data, classification makes information searchable, sortable, and ready for your spreadsheet to analyze.

To see how this works in practice, let's consider a common scenario. You're a project manager, and you receive this long, detailed email from a key client about a project deliverable:

Manually reading and digesting this could take several minutes. Instead, you can ask Gemini for a summary. Your prompt might be: "Summarize the key points and action items from this email into three bullet points." The AI doesn't just look for keywords; it understands the client's tone, identifies the core issues (a missed deadline, a bug), and recognizes the requested next steps.

Simultaneously, you can ask Gemini to classify the email. Given a prompt like, "Categorize this email based on urgency and topic from the following options: [Urgent Bug Report, Standard Feedback, Project Inquiry, Non-urgent Update]," the model will analyze the content and reliably label it 'Urgent Bug Report.' Now, instead of a wall of text, you have a concise summary and a clear, actionable category that your workflow can use to trigger a notification or create a task.

While the technology is powerful, the quality of your results depends heavily on the quality of your instructions. Think of yourself as a manager delegating a task to a very smart, very literal assistant. Here are a few rules of thumb for getting the best results from Gemini:

Be Specific and Clear. Don't just say 'summarize this.' Say 'Summarize this email for a non-technical manager, focusing on the business impact and required decisions.' The more context you provide, the more relevant the output will be.

Define Your Categories Explicitly. When classifying, provide the exact list of categories you want the AI to choose from. This constrains the model and ensures you get consistent, predictable outputs that your spreadsheet or workflow can easily handle.

Guide the Format. Want a bulleted list? Ask for it. Need the output in a specific JSON format for easy processing? Tell the model the exact structure you need. We will explore this powerful technique in detail when we start building our scripts.

So, to recap, we've broken down the AI 'magic' into two understandable and incredibly useful skills: abstractive summarization to get the gist of any text, and classification to turn messy information into organized data. These aren't just abstract concepts; they are the fundamental actions we'll be programming into our Google Workspace automations.

Understanding this foundation is crucial. Now that you know what the AI is doing, we're ready to make it happen. In the next section, we’ll open up Google Apps Script for the first time, connect to the Gemini API, and write our very first function to summarize a piece of text. It's time to go from theory to code.

References

1. Vaswani, A., et al. (2017). Attention Is All You Need. arXiv:1706.03762.
2. Google. (2024). Gemini API Documentation. Retrieved from ai.google.dev.
3. Brown, T. B., et al. (2020). Language Models are Few-Shot Learners. arXiv:2005.14165.
4. Zhang, Y., et al. (2020). A Survey on Neural Text Summarization. arXiv:2004.13289.
5. Minaee, S., et al. (2021). Deep Learning based Text Classification: A Comprehensive Review. arXiv:2004.03705.