Transforming Data Extraction with AI-Powered Intelligent Web Scraping

Abstract

The efficacy of web scraping techniques is often disproved by the complexity of modern web sites including dynamically generated pages, anti-scraping mechanisms, and contextual awareness needs. Traditional web scraping techniques rely on hard-coded rules and parsing approaches, which limit their responsiveness and accuracy. In order to overcome such problems, this research suggests an AI-driven web scraping system based on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) that scrapes, processes, and summarizes web data with the least amount of human intervention. The system enhances the web scraping process by integrating Selenium and BeautifulSoup for data retrieval, FAISS indexing for retrieving similarities, and transformer-based architectures for intelligent summarization. Using context-aware retrieval and text processing automation, the system generates quality, semantically dense, and structured outputs. The evaluation process involved experimentation using various datasets, accuracy assessments by ROUGE metrics and Cosine Similarity, and comparison to traditional scraping mechanisms. According to the experiments conducted, the proposed system is demonstrated to greatly improve data extraction speed and relevance of context. For example, news articles obtained a cosine similarity score of 90% and an ROUGE-1 score of 85%, confirming the accuracy and brevity of summaries produced by the system. The results demonstrate that the use of LLMs and RAG with web scraping enables more scalable, dynamic, and intelligent data extraction technologies.