How to Build an AI News Monitor: Scrape, Summarize, and Score Industry Stories Automatically

How to Build an AI News Monitor That Summarizes and Scores Industry Stories Manually reading industry news is expensive: 30-60 minutes/day tracking tech, competitor, and market stories. Most of it irrelevant. A few articles highly relevant. Here's how to build an automated news monitor that scrapes, summarizes, and scores news relevance — using Python and LLMs. What We're Building A pipeline that: Scrapes news from TechCrunch, Reuters, Google News, and custom RSS feeds Filters by keywords/topics relevant to your industry Summarizes each article in 2-3 sentences Scores relevance (1-10) and sentiment (positive/negative/neutral) Delivers a daily digest via email or Slack Step 1: Scraping News Sources Option A: RSS Feeds (Simplest) import feedparser import requests from datetime import datetime, timedelta import time # RSS feeds for common tech/business news RSS_FEEDS = { "TechCrunch": "https://techcrunch.com/feed/", "Reuters Technology": "https://feeds.reuters.com/reuters/technologyNews",