Effective Summarization Practices

Why AI Struggles with Summarization and How to Fix It Ever tried using ChatGPT or Claude to summarize a complex document, only to find the summary lacking or even misleading? You’re not alone. Researchers recently uncovered a key issue: AI often suffers from a “lost in the middle” problem. These models are great at picking up the start and end of a document but often miss crucial details in between. It’s akin to how humans remember the last thing they heard better, even when it’s not the most important. A new study dug deep into how well large AI models handle extensive texts. Despite claims that some models can process up to a million tokens (about 750K words), the reality is more nuanced. Many of these models struggle to connect ideas beyond a few thousand tokens. I have witnessed this firsthand while summarizing dense 50-page+ reports and papers. And it’s not just text-based models that have this issue. Multimodal models, which process text, images, and audio, face the same challenges. After comparing an AI-generated summary to the original, I have found that sometimes the AI didn’t truly summarize; it just shortened the text, often missing key information and sometimes introducing errors. If you’re relying on AI for summarization, here’s how to get better results: 1. Divide and Conquer: Break your text into smaller chunks and summarize each separately. 2. Be Specific: Use targeted prompts that ask for all concrete facts, figures, and insights, preferably in bullet points for easy verification. 3. Follow Up: Don’t hesitate to ask follow-up questions like, “Did you miss anything?” or “Is all this information accurate?” 4. Verify: Always cross-check key points with the original text to ensure accuracy and avoid hallucinations. In the rush to leverage AI, we can’t afford to overlook its limitations. By being mindful of these quirks, we can make AI a more reliable partner in our work. Study link here- https://lnkd.in/eWk6kyZx #AI #ChatGPT #AIResearch #Innovation #TechTips

🚨 "Just increase the context length!" is often the knee-jerk response to LLM data challenges. But throwing more tokens at the problem isn't always the answer - here's why. After working extensively with LLMs, I've learned that smartly handling data is far more crucial than simply cramming more into the context window. Here's my critical take on managing large datasets with LLMs: 1. Quality Over Quantity The real challenge isn't fitting more data - it's identifying what data actually matters. I've seen teams waste resources trying to expand context when they should have been refining their input. 2. The Hidden Costs Longer context = higher compute costs and slower responses. Sometimes, a well-designed RAG system with smaller chunks is more efficient than maxing out your context window. 3. Smart Chunking > Brute Force Before reaching for those expensive extended context models, consider if your data can be intelligently split and processed in stages. Often, it can. 4. The Quantization Trap A common misconception: quantized models don't increase context length. They make processing more efficient, but won't magically let you fit more tokens in context. 5. Context Length ≠ Understanding I've observed that models sometimes struggle more with longer contexts, leading to inconsistent outputs and hallucinations. Sometimes less really is more. 🛠️ Practical Techniques That Actually Work: 1. Recursive Summarization: - Create summaries of chunks (500 tokens each) - Summarize those summaries - Keep going until you have a manageable size - Works great for long documents! 2. Smart Preprocessing: - Remove redundant sections - Strip formatting that doesn't add value - Use regex to clean noisy data - Extract key information using rule-based systems 3. Hybrid Approaches: - Combine embedding search with metadata filtering - Use traditional NLP for initial filtering - Let the LLM handle the refined dataset - Store structured data separately from text 4. Advanced RAG Strategies: - Implement sentence window retrieval - Use hybrid search (keyword + semantic) - Maintain context across multiple queries - Cache common retrievals Remember: The goal isn't to fit more data into the context - it's to get better results from your LLM. Sometimes that means being selective about what you feed it. Pro Tip: Start with a small, well-curated dataset and gradually scale up. You'll be surprised how often the "minimal viable context" outperforms larger, noisier inputs. What strategies have you found effective for managing large datasets with LLMs? Let's discuss below! #AI #MachineLearning #LLM #DataScience #ArtificialIntelligence #TechTips

Throwing everything into the prompt is easy. But that’s why most LLM summaries suck. Let me explain... This approach quickly runs into problems: • Context window limits that cut off important information • Reduced performance because the model struggles to find signal in a noisy haystack of data The good thing is advanced summarization techniques exist to handle larger, more complex datasets. Let's look at 3 proven strategies you should know: 𝟭. 𝗦𝘁𝘂𝗳𝗳𝗶𝗻𝗴 The simplest method - throw everything into one prompt. This works well for very small datasets that fit inside the model’s context window. But as your documents get bigger, this method quickly breaks down. 𝟮. 𝗠𝗮𝗽-𝗥𝗲𝗱𝘂𝗰𝗲 Break your data into smaller chunks, summarize each chunk separately ("map”), then combine those summaries into one polished version (“reduce”). This method works much better for large documents and keeps your summaries clear and manageable. 𝟯. 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝗶𝗻𝗴 Group similar documents by topic before summarizing. Clustering helps you capture the big picture and create summaries focused on theme. This reveals insights simpler methods miss (and is especially useful for large or varied collections of documents). 𝗔𝗻𝗱 𝘁𝗵𝗲 𝗴𝗼𝗼𝗱 𝗻𝗲𝘄𝘀? In the latest lesson of the 𝗣𝗵𝗶𝗹𝗼𝗔𝗴𝗲𝗻𝘁𝘀 course, we explore all these techniques. We focus on stuffing for short texts but also show how to train your model to use map-reduce and clustering through instruction tuning. We also built a dataset pipeline that gathers data from Notion and web crawls, preparing it for strong, flexible summarization. Want to learn more? Check the link in the comments.

 Text Summarization for Large Files – Solving the Context Limit Challenge When working with large documents like 20-page legal files, lengthy research papers, or even entire books, direct summarization using foundation models often fails—why? Because of context length limitations. That’s where LangChain and smart architectures like MapReduce summarization come into play. 🧠 Here’s how it works (at a glance): 1 - Input: A large document (e.g., 500-page book) 2 - Chunking: File is split into manageable parts (with overlap to preserve meaning) 3 - Mapping: Each chunk is summarized independently by the model 4 - Reducing: Summaries are then combined into one coherent summary 5 - Result: A clear, concise summary of a document the model couldn’t process all at once 🔍 Why MapReduce Works So Well: Scalable: Handles files of any size Efficient: Fewer calls to the model = lower compute cost Accurate: Overlapping chunks reduce info loss Coherent: The final summary ensures a unified narrative 💬 Curious how this applies to your workflows? We explore this and other hands-on strategies in my book: 📘 Generative AI for Software Developers - https://lnkd.in/gfGa4_9z Learn how to build smart, scalable, and efficient AI-powered apps, from summarization to automation and beyond. #LangChain #GenerativeAI #TextSummarization #LLMs #AIinPractice #AItools #LegalTech #ResearchTools #AIforDevelopers #MapReduce #SoftwareDevelopment

🔍 Simplifying LLM Chunking Strategies: Mastering the Art of Efficient Text Processing As the adoption of Large Language Models (LLMs) continues to revolutionize industries, handling large text inputs effectively remains a crucial challenge. LLMs have strict token limits, and feeding them large amounts of text often requires a thoughtful approach to break it down without losing meaning. This is where LLM Chunking Strategies come into play—helping process text into manageable, coherent pieces for better results. Here’s a deep dive into the three key chunking strategies commonly used: 1️⃣ Fixed-Size Chunking This approach splits text into fixed-sized chunks based on the number of tokens or characters. Advantages: Simple and straightforward to implement. Drawbacks: It lacks semantic coherence, as the chunks often break sentences or paragraphs abruptly, leading to incomplete context. Best for: Quick and basic tasks where maintaining semantics isn’t a priority. 2️⃣ Sliding Window Approach This method involves moving a fixed-size window across the text, creating overlapping chunks that capture contextual overlap between consecutive pieces. Advantages: It ensures the preservation of context across chunks, avoiding abrupt transitions. Drawbacks: While effective, it introduces redundancy since the overlapping sections repeat across chunks, increasing computational cost. Best for: Use cases where context continuity is critical, such as summarization or question answering. 3️⃣ Recursive Splitting Unlike the other methods, this strategy divides text recursively based on semantic or structural boundaries, such as sentences, paragraphs, or logical sections. Advantages: It ensures the integrity of meaning, as chunks are formed around natural language breaks. Drawbacks: Requires advanced parsing techniques and can be computationally intensive to implement. Best for: Complex tasks like document summarization or analysis that demand high semantic accuracy. Why Does This Matter? Choosing the right chunking strategy can significantly impact the performance and accuracy of LLM-based applications. While fixed-size chunking is easy to implement, it might fall short in maintaining meaning. Sliding windows add contextual continuity but at the cost of efficiency. Recursive splitting offers semantic richness but requires careful execution. 💡 Pro Tip: For most real-world applications, a combination of these strategies often works best, tailored to your specific use case. Which chunking strategy do you find most useful in your work? Share your experiences and thoughts in the comments! Let’s brainstorm and refine our approaches to unlock the true power of LLMs. 🚀

I developed a simple system for making LLMs better at summarizing meeting notes. I'd encourage you to use it. I use symbols to add important context to the notes I am writing, identifying what I view as important or warranting further research rather than relying on the LLM to figure that out on their own. Its a great example of us working together and me micromanaging my little intern as is fit to do. Please steal this. I am including the prompt I use for meeting note summarization. --Prompt-- Please summarize the raw meeting notes included after the "//" below according to these guidelines: Executive Summary: Start with a brief overview of the meeting's main objectives and outcomes. Important Points: List all important points marked with '*'. Organize them clearly under this section. Key Sections: Split the notes into sections based on the topics discussed. Elaborate on each key point within its respective section. Action Items: Include all action items marked with '^', specifying who is responsible for each action. List them under 'Action Items'. Follow-Up Questions/Research: List any follow-up questions or items needing further research marked with '?', under 'Follow-Up Items'. Verbatim Quotes: Incorporate any verbatim quotes noted by double quotes ("") in relevant places throughout the summary in italics. Include all quotes, as verbatims are important! Style Guidelines: Conciseness: The summary should be as concise as possible while maintaining the meaning of the notes. Language: Avoid using flowery or superfluous words. Tone: Write in a neutral, colloquial tone. Recommendations: Suggest any additional meetings or actions if necessary. // <meeting notes>