YouTube Summarizer GenAI: An Intelligent Video Content Summarization System Based on Large Language Models

This article introduces the open-source project YouTube Summarizer GenAI, an end-to-end generative AI application that integrates data extraction, text preprocessing, and large language model capabilities to convert YouTube videos into structured, readable summaries. It addresses the inefficiency of video content consumption and provides users with an intelligent tool to quickly access core information.

In the era of information explosion, YouTube uploads over 70 million hours of video daily, but videos have low "time density" (e.g., a 30-minute video may only contain 5 minutes of core content), leading to inefficient consumption. This dilemma has spurred a strong demand for video summarization tools, and YouTube Summarizer GenAI is the open-source solution created to address this need.

The project adopts a three-stage pipeline:

1. Data extraction: Obtain auto-generated or uploaded subtitles via the YouTube Subtitle API;
2. Text preprocessing: Clean noise (timestamps, repeated segments, filler words, etc.) and correct recognition errors;
3. LLM summary generation: Use prompt engineering to control style, length, and format. Technical components include: Using YouTube Data API/third-party libraries to get subtitles (no download required, multilingual support); Supporting GPT series, Llama, and other models (flexible choice between commercial and open-source); Well-designed prompts (role setting, task description, format specifications, etc.).

This tool is applicable to:

• Educational learning: Students quickly get key course points to generate notes;
• Technical research: Practitioners screen high-value videos;
• Content creation: Creators reference inspiration or generate supporting materials;
• Accessibility: Hearing-impaired or non-native speakers can consume content more easily.

Challenges and solutions:

1. Uneven subtitle quality: Use context-based correction, combine title and description semantics, and enhance domain terms;
2. Long video processing: Segment processing then integration;
3. Summary quality evaluation: ROUGE/BLEU automatic metrics, manual evaluation, and user feedback loop.

Features: End-to-end pipeline (no manual intervention), modular design (replaceable components), configurability (custom prompts/models/formats), open-source friendly. Future directions: Multimodal summarization (combining video frames/audio), interactive summarization (conversational exploration), personalized summarization (user preference customization), real-time summarization (live streaming scenarios).

YouTube Summarizer GenAI represents a new paradigm of AI-enabled content consumption: It provides efficient choices (read summaries when short on time, watch full videos when time allows), making information consumption more flexible. For developers, it is a good case to learn LLM application building. In the future, with the progress of LLMs, video summary quality will continue to improve, moving toward AI systems that can understand content and extract knowledge.