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Users can select between:  \n- **Extractive Summarization**: Extracts key sentences from the input text using the **TF-IDF algorithm**.  \n- **Abstractive Summarization**: Generates human-like summaries using HuggingFace’s **T5 Transformer model**.\n\n---\n\n## 🚀 Features\n\n- 🔄 **Dual Summarization Modes**:  \n  - **Extractive**: Highlights the most important sentences from the text.  \n  - **Abstractive**: Creates entirely new sentences to summarize the content.  \n- 💻 **Streamlit-based UI**: A clean, interactive interface for inputting and summarizing text.  \n- 🖱️ **Easy-to-Use**: Simply paste your text, select the summarization type, and get the summary at the click of a button.\n\n---\n\n## 🛠️ Tech Stack\n\n- **Programming Language**: Python 🐍  \n- **Libraries and Tools**:  \n  - `nltk`: Tokenization and stopword removal.  \n  - `transformers`: HuggingFace's T5 model for abstractive summarization.  \n  - `streamlit`: Intuitive UI for user interaction.  \n- **Algorithms**:  \n  - **TF-IDF**: For extractive summarization.  \n  - **HuggingFace's T5-small Transformer**: For abstractive summarization.\n\n---\n\n## 🧠 How It Works\n\n1. **Extractive Summarization**  \n   - Tokenizes the text and computes word frequencies, ignoring stopwords and punctuation.  \n   - Scores sentences based on the word frequencies.  \n   - Selects the top sentences to generate a summary.\n\n2. **Abstractive Summarization**  \n   - Uses the HuggingFace **T5-small Transformer** model to understand and generate a concise version of the input text.  \n   - Produces summaries that feel natural and coherent.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikhilchaudhary1%2Ftext-summarizer","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnikhilchaudhary1%2Ftext-summarizer","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnikhilchaudhary1%2Ftext-summarizer/lists"}