Text Summarization with Deep Learning 🧠

Text summarization is a critical NLP task that leverages deep learning to condense long documents into concise versions. Below are key concepts and resources:

🔑 Core Techniques

• Sequence-to-Sequence Models Early approaches using RNNs and attention mechanisms (e.g., [Seq2Seq](/en/resources/sequence_to_sequence))
• Transformer Architecture State-of-the-art models like BERT, T5, and [GPT-3](/en/resources/gpt_3)
• Pre-trained Language Models
  Fine-tuning models on summarization tasks (e.g., RoBERTa)

📚 Popular Frameworks

• PyTorch: Text Summarization Tutorial
• TensorFlow: Transformer Implementation Guide
• Hugging Face Transformers: Model Hub

🌐 Applications

• News article condensation
• Research paper abridgment
• Chatbot response compression
• Legal document simplification

📌 Further Reading

For advanced topics, explore:

1. Deep Learning for NLP
2. Attention Mechanisms Explained
3. Ethical Considerations in AI

Let me know if you'd like to dive deeper into any specific model or application!