Machine Learning Internship Challenge

Create a text summarization system using transformers with Python.

Main Goal:

The system's goal is to quickly create short summaries that give all the important information from long articles / Paragraphs.

Technology / methodology :

Create a webpage using HTML/CSS where users can type a lot of text.
Write a program in Python that can summarize text using a library called Transformers.
Instead of starting from scratch, use a model that's already been trained on a lot of text data. Example Models like BERT or GPT are really smart and can help us summarize text effectively.
Making use of API is a Plus point.

Requirement:

User Interface for Summarization: Develop a user interface (webpage with a form field) allowing users to input long-form text or documents.

Data Collection:

Collect a dataset of long-form articles or documents across diverse topics for training and testing the summarization model.

Data Preprocessing:

Preprocess the text data, including tokenization, removing unnecessary formatting, and handling special characters. (breaking text into words or subwords).

Train-Test Split:

Divide the dataset into training and testing sets to evaluate the model's performance accurately.

Pre-trained Transformer Model:

Choose a pre-trained transformer model, such as BERT or GPT, suitable for text summarization tasks.

Fine-tuning:

Fine-tune the chosen pre-trained model on the training dataset using the summarization task objective, adjusting the model for the specific summarization context.

Integration with External Tools (Optional):

Integrate the summarization system with external tools or applications, allowing users to access summaries seamlessly within their preferred platforms.

Continuous Learning:

Implement a mechanism to fine-tune the model periodically with new summarization data, adapting to evolving language patterns and improving summarization quality.

Natural Language Understanding (Optional):

Enhance the summarization system with a natural language understanding module, using transformers for entity recognition or extracting key phrases.

Security and Privacy:

Implement measures to secure user data and ensure privacy, especially when handling sensitive information within documents.

Testing and Evaluation:

Test the summarization system with a variety of articles, assessing the quality of generated summaries. Evaluate the system's performance using metrics such as ROUGE scores for summarization tasks.

Deployment / Release:

Deploy the text summarization system, making it accessible through an API or a web interface, allowing users to efficiently extract key information from lengthy documents using the power of transformer-based models.

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