QuickSumm : Text Summarizer

Named Entity Recognition (NER) Model with Integrated Text Summarizer

This repository contains an integrated solution for Named Entity Recognition (NER) coupled with a text summarizer to efficiently extract and summarize key information from unstructured text data.

Problem Statement

Named Entity Recognition (NER) identifies entities such as people, organizations, and locations in text. However, NER alone may not provide actionable insights. Integrating NER with a text summarizer is crucial for effectively distilling essential information. The challenge is to develop a robust NER model capable of recognizing entities across various domains and languages while summarizing them effectively. This integrated solution aims to enhance information extraction, aiding tasks such as document analysis and information retrieval.

Solution Overview

Our solution integrates state-of-the-art NER techniques with advanced text summarization algorithms. By combining these components, we aim to create a comprehensive tool for extracting and summarizing key information from unstructured text data.

Key Features:

• Named Entity Recognition (NER): Utilizes advanced algorithms to identify entities such as people, organizations, and locations in text.

• Text Summarization: Implements cutting-edge techniques to generate concise summaries of extracted entities and their contexts.

• Domain and Language Agnostic: The NER model is designed to recognize entities across various domains and languages, ensuring versatility and adaptability.

• Enhanced Information Extraction: By integrating NER with a text summarizer, our solution enhances information extraction efficiency, aiding tasks such as document analysis and information retrieval.

Installation

1. Clone this repository:

   git clone https://github.com/yourusername/ner_text_summarizer.git

2. Navigate to the project directory:

   cd ner_text_summarizer

3. Install dependencies:

   pip install -r requirements.txt

Usage

Example Usage

1. Navigate to the examples directory:

   cd examples

2. Run the NER model on an example text file:

   python ../ner_text_summarizer/ner_model.py example.txt

3. Run the text summarizer on the same example text file:

   python ../ner_text_summarizer/text_summarizer.py example.txt

Snapshots of QuickSumm

• Input Field

• Output Field

• Sample test case

• Dark mode functionality

Contributing

Contributors:

• Prajakta Darekar
• Rutuja Chougule
• Pooja Hanamane
• Arya Jagdale

Contributions are welcome! If you have any suggestions or improvements, feel free to open an issue or submit a pull request.