Information Extraction and Summarization System using Big Data and AI
Developed system for information extraction and summarization using AI and big data techniques for financial industry applications
Overview
This project (January 2018 – February 2019) developed an AI-powered system for automatically extracting and summarizing relevant information from large volumes of unstructured financial data. The goal was to help financial analysts and researchers quickly find actionable insights from the growing flood of financial documents, news, and reports.
Technical Approach
The system combined natural language processing with big data processing techniques to build an end-to-end pipeline: from raw document ingestion through entity extraction, relationship identification, and automated summarization. We focused on Korean financial texts, addressing the unique challenges of Korean morphological analysis in a financial domain context.
Key components included specialized named entity recognition for financial terms, automated document classification, and extractive summarization optimized for financial report formats.
Applications
The system was designed to serve the financial industry, particularly in areas like investment research, market monitoring, and regulatory compliance — where analysts routinely process thousands of documents and need tools to surface the most relevant information quickly.
Collaborators
- Sogang University: Lead research institution
- WISEfn: Industry partner providing financial data expertise
- Research Team: Jung Yu Sin, Lee Young Joon