Text generation and text summarization are two distinct tasks in natural language processing (NLP), each with unique objectives and methodologies.

Text generation is the process of creating coherent and contextually relevant content from a prompt or input. It can range from completing a sentence to writing entire articles, poems, or stories. Generative models like GPT (Generative Pretrained Transformer) are commonly used for this task. These models are trained on vast datasets to learn patterns of language, allowing them to generate new, human-like text. Applications include content creation, chatbots, coding assistance, and more.

In contrast, text summarization involves distilling a large body of text into a shorter version while preserving the original meaning and important information. Summarization can be extractive, where key sentences or phrases are pulled directly from the source, or abstractive, where the model rewrites the content in a new form, often using different words and structure. This requires a deep understanding of the context, key points, and semantics.

The core difference lies in their goals: generation creates new content, while summarization condenses existing information. While both use advanced NLP models, summarization demands more contextual awareness and content comprehension to ensure accuracy, whereas generation emphasizes fluency, creativity, and contextual relevance.

Both tasks leverage deep learning models like transformers, and advances in Generative AI have significantly improved their performance. A solid understanding of these tasks is essential for anyone pursuing a Gen AI and machine learning certification, as they represent foundational applications in real-world AI solutions.