Problem Statement
The modern news environment produces an enormous and continuously changing stream of information from different publishers and data providers. A single event can be reported by many sources, often with overlapping or near-identical articles.
This creates two practical problems: collecting information from heterogeneous sources and reducing redundant coverage before it can be meaningfully analyzed.
For an intelligence-oriented platform, simply gathering articles is not enough. The underlying data needs to be consolidated into a structured dataset where duplicate or near-duplicate reports can be identified and collapsed, creating a cleaner foundation for subsequent analysis of news events.
Proposed Solution
IntelliBrief was developed as a Python-based news ingestion and data-processing layer designed to aggregate information from multiple sources into a unified intelligence dataset.
The ingestion system collects articles from:
- RSS / Atom feeds
- NewsAPI
- GDELT
The incoming articles are processed and stored in SQLite, with a near-duplicate detection layer used to identify overlapping reports and reduce redundant entries.
Data Pipeline
Multiple News Sources → Ingestion → Normalization → Near-Duplicate Detection → SQLite
This creates a centralized and cleaner representation of the incoming news stream rather than treating each source as an isolated feed.
Next Intelligence Layer
The next stage of the system is an embeddings-based event clustering engine. The purpose of this layer is to move beyond individual articles and group semantically related reports around the same underlying event.
For example, multiple articles describing the same geopolitical development could eventually be represented as one developing event with multiple source reports, rather than as unrelated articles.
Core Idea
From a stream of articles to a structured view of events.
Technology Stack
Python · RSS / Atom · NewsAPI · GDELT · SQLite · Embeddings · NLP · Data Engineering