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

Introduction

The Information Extraction and Summarization System project represents a comprehensive initiative to develop advanced AI-powered systems for extracting and summarizing relevant information from large amounts of unstructured data, specifically designed for financial industry applications.

Objective

Develop a sophisticated system that can automatically extract and summarize relevant information from vast amounts of unstructured data, utilizing cutting-edge big data and artificial intelligence techniques to improve efficiency in information gathering and decision-making processes within the financial industry.

Key Features

  • Information Extraction: Advanced algorithms for identifying and extracting relevant information from unstructured data
  • Automated Summarization: AI-powered summarization of large documents and datasets
  • Big Data Processing: Scalable processing capabilities for large-scale data analysis
  • Financial Industry Focus: Specialized tools and techniques for financial data analysis
  • Decision Support: Enhanced decision-making capabilities through automated information processing

Technical Approach

  • Natural Language Processing: Advanced NLP techniques for text analysis and understanding
  • Machine Learning: ML algorithms for pattern recognition and information extraction
  • Big Data Technologies: Scalable processing frameworks for large datasets
  • Text Mining: Specialized techniques for extracting insights from textual data
  • Summarization Algorithms: Advanced algorithms for automated document summarization

Applications

  • Financial Analysis: Enhanced analysis of financial documents and reports
  • Market Research: Automated processing of market information and trends
  • Risk Assessment: Improved risk analysis through automated information processing
  • Investment Research: Enhanced capabilities for investment decision-making
  • Regulatory Compliance: Automated processing of regulatory documents and requirements

Impact

  • Efficiency Improvement: Significant reduction in time required for information processing
  • Decision Enhancement: Improved decision-making through comprehensive information analysis
  • Cost Reduction: Reduced manual effort in information gathering and analysis
  • Accuracy Improvement: Enhanced accuracy in information extraction and summarization
  • Scalability: Ability to process large volumes of data efficiently

Technical Components

  • Data Processing Pipeline: End-to-end processing workflow for information extraction
  • Machine Learning Models: Advanced ML models for pattern recognition and classification
  • Text Analysis Engine: Specialized engine for natural language processing
  • Summarization System: Automated summarization capabilities
  • User Interface: Intuitive interface for system interaction and results visualization

Research Areas

  • Information Extraction: Advanced techniques for identifying relevant information
  • Text Summarization: Automated summarization algorithms and methods
  • Big Data Analytics: Scalable processing techniques for large datasets
  • Financial NLP: Specialized natural language processing for financial applications
  • Decision Support Systems: AI-powered systems for decision-making support

Collaborators

  • Sogang University: Lead research institution
  • WISEfn: Industry partner and financial expertise
  • Research Team: Jung, Yu Sin and Lee, Young Joon
  • Industry Partners: Financial industry collaboration and testing