Automatic Summarization Processes

It’s easy to treat AI summaries like magic. But under the hood, it’s a structured, multi-step pipeline grounded in engineering. Once you understand how these systems work, you will realize that not all AI Assistants are the same - the choices each system makes around system prompts, semantic segmentation, and workflows matter quite a bit. Put another way, if you are Chief of Staff, you shouldn’t be using an AI Assistant built for SDRs. Here’s the typical flow: 1. Real-time transcription: Audio from Zoom/Meet/Teams is captured live. ASR (Automatic Speech Recognition) models like Whisper or AssemblyAI convert it to text. These models are trained on millions of hours of speech and handle accents, overlaps, and filler words in real-time. 2. Speaker diarization: The transcript is then split by speaker. Voiceprint embeddings + timestamp clustering identify who said what. This helps anchor decisions and actions to actual participants. 3. Semantic segmentation: The raw transcript is parsed into segments: agenda items, decisions, questions, blockers. This uses lightweight topic segmentation (e.g., TextTiling or transformer-based classification). This is where role based AI Assistants really start to show their intent. 4. Abstractive summarization: This is where LLMs kick in. Instead of just picking key sentences (extractive), models like GPT-3.5 or fine-tuned PEGASUS generate condensed, human-readable summaries: - What was discussed - What was decided - What needs follow-up 5. Intent + entity extraction (optional): Some pipelines go further, tagging action items, deadlines, owners, using NER + intent classifiers trained on meeting-specific corpora. 6. Output formatting: Finally, everything is structured into a standard schema (JSON or markdown). This makes it easy to post to any downstream system: a Slack channel, a Notion page, a CRM log. In the end, it’s not “AI wrote this.” It’s: --> ASR + NLP + LLM + a lot of thoughtful engineering orchestration, often built for a specific persona. One obvious tip - if you are paying for an AI Assistant, make sure that the system prompts under the hood were written for your role.

I’ve been working with GenAI for 3+ years. Here’s something all engineers must come to terms with: If you’re building LLM-powered applications, at some point, you’ll need to generate high-quality datasets to fine-tune SLMs. Why? → Fine-tuning SLMs reduces costs, latency, and throughput while maintaining high accuracy for specific tasks. → Some domains require specialized fine-tuning for better domain adaptation. → Fine-tuned models give you more control over AI behavior and response generation. That’s exactly what we’re tackling with our 𝗦𝗲𝗰𝗼𝗻𝗱 𝗕𝗿𝗮𝗶𝗻 𝗔𝗜 𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁. ... and today, I’m breaking down the dataset generation feature pipeline we built for fine-tuning our summarization SLM. The input to our generation pipeline will be raw documents from MongoDB (Notion & crawled resources). And the output is a high-quality summarization dataset published to Hugging Face’s dataset registry. Since this pipeline generates features used to train an LLM, it’s called a feature pipeline. Here’s how it works, step by step: 𝟭. 𝗗𝗮𝘁𝗮 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗶𝗼𝗻 → Pulls raw documents from MongoDB and standardizes formatting. 𝟮. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 → Analyzes length & quality scores distributions to make informed decisions. 𝟯. 𝗗𝗮𝘁𝗮 𝗙𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴 → Removes low-value content, keeping only high-quality documents. 𝟰. 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 → We use a more powerful LLM (e.g., `gpt-4o`) to generate multiple summaries per document by varying temperature and sampling parameters (a process known as distillation) 𝟱. 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 → Filters out poor-quality summaries. 𝟲. 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗦𝗽𝗹𝗶𝘁𝘁𝗶𝗻𝗴 → Divides data into training, evaluation, and test sets (done before storing the dataset and not at training time!) 𝟳. 𝗩𝗲𝗿𝘀𝗶𝗼𝗻𝗶𝗻𝗴 & 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 → Publishes the final dataset to Hugging Face. To keep the pipeline reproducible, trackable, and scalable, we manage it using ZenML, which: → Orchestrates the entire workflow from extraction to deployment. → Ensures traceability & versioning of pipeline runs & datasets. → Allows dynamic configuration for different filtering, summarization & structuring techniques. Even if you’re not deep into fine-tuning, at some point, you’ll need a structured way to generate datasets for specialized AI applications. This is one of the most critical components of your pipeline. Want to learn more? Check out the link in the comments.

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

Building a Visual AI Agent for Video Search and Summarization with NVIDIA AI Blueprint. Building a visual AI agent capable of understanding long-form videos requires a combination of VLMs and LLMs ensembled together with datastores. Nvidia AI's blueprint provides a recipe for combining all of these components to enable scalable and GPU-accelerated video understanding agents that can perform several tasks such as summarization, Q&A, and detecting events on live streaming video. 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗖𝗼𝗺𝗽𝗼𝗻𝗲𝗻𝘁𝘀 i) Stream handler:  - Manages the interaction and synchronization with the other components such as NeMo Guardrails, CA-RAG, the VLM pipeline, chunking, and the Milvus Vector DB. ii) NeMo Guardrails - Filters out invalid user prompts. It makes use of the REST API of an LLM NIM microservice.  iii) VLM pipeline - Decodes video chunks generated by the stream handler, generates the embeddings for the video chunks using an NVIDIA Tensor RT-based visual encoder model, and then makes use of a VLM to generate per-chunk response for the user query. It is based on the NVIDIA DeepStream SDK.  iv) VectorDB - Stores the intermediate per-chunk VLM response. v) CA-RAG module - Extracts useful information from the per-chunk VLM response and aggregates it to generate a single unified summary. - CA-RAG (Context Aware-Retrieval-Augmented Generation) uses the REST API of an LLM NIM microservice. vi) Graph-RAG module - Captures the complex relationships present in the video and stores important information in a graph database as sets of nodes and edges. This is then queried by an LLM for interactive Q&A. 𝗩𝗶𝗱𝗲𝗼 𝗶𝗻𝗴𝗲���𝘁𝗶𝗼𝗻 i) VLM pipeline and CA-RAG -  create smaller chunks from long videos, analyze the chunks individually using VLMs to produce dense captions, and then summarize and aggregate results to generate a single summary for entire file. ii) Knowledge graph and Graph-RAG module -  a knowledge graph is built and stored during video ingestion. - By using Graph-RAG techniques, an LLM can access this information to extract key insights for summarization, Q&A, and alerts 𝗩𝗶𝗱𝗲𝗼 𝗿𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 - For each of the tasks like summarization, Q&A etc., blueprint exposes simple REST APIs that can be called to integrate with your application - A reference UI is also provided to enable you to quickly experiment with the features of the blueprint and tune the agent 𝗦𝘂𝗺𝗺𝗮𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻 summarize endpoint is called to get a summary, with following prompts: - Prompt (VLM): Prompt given to the VLM to produce dense captions - Caption summarization (LLM): An LLM prompt used to combine the VLM captions - Summary aggregation (LLM): produces final summary output based on the aggregated captions 𝗥𝗲𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝗱 𝗕𝗹𝗼𝗴: https://lnkd.in/eFEn4akG 𝗔𝗜 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁: https://lnkd.in/eP36EJ65

 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

I asked an LLM to summarize a 30,000-word podcast transcript for me. It nailed the beginning. It did ok on the end content. The middle content disappeared. 🎯 This is called the Lost-in-the-Middle Problem. To test this phenomenon, I took hundreds of podcast transcripts and broke them into 10% chunks and scored how well LLM summaries covered each section. Middle sections consistently scored the lowest in information retained. The longer the document, the worse it got. A 30,000-word transcript loses far more from the middle than a 10,000-word one. The models aren't broken. It's just prioritizing the edges of the prompt. 📈 The Fix: Prompt Chaining Instead of one massive prompt to summarize, I restructured the prompt into a chain. The idea was to summarize each 10% chunk separately, one at a time. The model is less likely to skip anything when it only sees one piece at a time. Same LLM. Smarter structure. Measurably better output. Trade-off: 10 API calls instead of 1. Worth it when information retention matters more than speed. 🔍 The harder problem was measuring it. There's no "correct summary" to compare against and there's no accepted metric for a "good summary". So I had to use a proxy — compare embeddings of each source chunk against the summary chunks. Higher cosine similarity = more information survived. Is this perfect? No but it scales pretty well. This is from Chapter 3 of Building Agentic AI — a free Substack series I'm co-writing with Julian Alvarado. Full post + code linked in the comments. Your prompts are already losing the middle. You just might not have measured it yet. #AgenticAI #LLM #RAG