- Text summarization

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Text summarization is a fundamental task within the realm of natural language generation, encompassing a wide range of applications and domains. One of the most promising applications of language models is the capacity to condense complex articles and ideas into concise, easily digestible summaries. This process can take various forms, adapting to different flavors and subjects. Whether it's news articles, research papers, or educational content, text summarization provides a valuable tool for quickly extracting key information.

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To facilitate text summarization, prompts serve as a powerful means of instructing the model. Imagine you're researching antibiotics, and you come across a lengthy article on the topic. This article contains detailed information about how antibiotics work, their forms, their limitations, and potential risks like antibiotic resistance. To quickly grasp the essential points from this extensive content, you can employ text summarization techniques.

You start by using a prompt like "Explain antibiotics." The language model then generates a detailed response, summarizing various aspects of antibiotics, such as how they function, their different forms, and their limitations.

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However, if you find the initial response too lengthy and want a more concise overview, you can provide a different prompt: "Summarize the above information in one sentence." The model will then distill the key points into a single, easily digestible sentence, giving you a quick summary of the article's main insights.

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This example demonstrates how text summarization, with the help of prompts, can efficiently extract and condense relevant information from extensive texts, making it a valuable tool for researchers, students, and anyone seeking quick access to essential knowledge.

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