Merge pull request #1599 from NimraAslamkhan/main

Text_Summarization_Using_Transformers

Machine Learning and Data Science/Basic/Text Summarization Using Transformers/Readme.md

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+# Text Summarization Using Transformers
+
+## Project Overview
+The **Text Summarization Using Transformers** project aims to automatically generate concise summaries from larger text documents using state-of-the-art natural language processing (NLP) techniques. This project leverages the power of pre-trained transformer models, specifically BART and T5, to produce high-quality summaries that capture the essence of the original texts.
+
+## Objectives
+- To generate brief summaries of longer texts.
+- To evaluate the quality of the generated summaries using quantitative metrics like ROUGE.
+
+## Technologies Used
+- **Programming Language**: Python
+- **Libraries**: 
+  - [Hugging Face Transformers](https://github.com/huggingface/transformers)
+  - [NLTK](https://www.nltk.org/) (for text processing)
+  - [ROUGE Score](https://github.com/google-research/google-research/tree/master/rouge) for evaluation
+- **Platform**: Google Colab for seamless execution and resource management
+
+## Dataset
+- The dataset used for this project consists of articles and their corresponding summaries. You can use datasets from news articles or Wikipedia.
+- Example datasets include:
+  - [CNN/Daily Mail Dataset](https://cs.nyu.edu/~thaddeus/projects/cnn/)
+  - [XSum Dataset](https://github.com/nyu-dl/dl4summarization)
+
+## Key Steps
+1. **Environment Setup**: 
+   - Use Google Colab to set up your environment.
+
+2. **Data Preprocessing**: 
+   - Load the dataset, clean the text, and prepare it for summarization.
+
+3. **Model Selection**: 
+   - Choose a pre-trained transformer model (e.g., BART or T5) from the Hugging Face Transformers library.
+
+4. **Summary Generation**: 
+   - Apply the selected model to generate summaries from the input texts.
+
+5. **Evaluation of Summary Quality**: 
+   - Use the ROUGE metric to quantitatively evaluate the performance of the generated summaries against reference summaries.
+# Text Summarization Using Transformers
+
+## Example Results
+
+### Original Article
+![Original Article](https://github.com/user-attachments/assets/aa27dc2d-6897-45a8-875c-dc8087683d66)
+
+
+### Generated Summary
+![Generated Summary](https://github.com/user-attachments/assets/0e81c519-a2a5-4eb5-aa79-70abeb445fe6)
+
+
+### Evaluation Scores
+![Evaluation Scores](https://github.com/user-attachments/assets/f9634d8e-9f63-480a-a6d0-67adfb8192e1)
+
+
+