Summarization [Neurosurgery Wiki]

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====== Summarization ======

Summarization is the process of condensing a larger body of [[text]] or [[information]] into a shorter, more concise version while retaining its essential meaning and key points. It can be done manually or using automated techniques, such as extractive summarization (selecting key sentences directly from the text) or abstractive summarization (generating a new summary using rewording and paraphrasing). Summarization is widely used in fields like journalism, education, research, and artificial intelligence to enhance readability and comprehension.
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[[ChatGPT]], a [[conversational artificial intelligence model]] based on the generative pre-trained transformer [[GPT architecture]], has garnered widespread attention due to its user-friendly nature and diverse capabilities. This [[technology]] enables users of all backgrounds to effortlessly engage in human-like [[conversation]]s and receive [[coherent]] and intelligible responses. Beyond casual interactions, ChatGPT offers compelling prospects for [[scientific research]], facilitating [[task]]s like [[literature review]] and content [[summarization]], ultimately expediting and enhancing the [[academic writing]] process. Still, in the field of medicine and surgery, it has already shown its endless potential in many [[task]]s (enhancing decision-making processes, aiding in [[surgical planning]] and [[simulation]], providing real-time assistance during [[surgery]], improving [[postoperative care]] and [[rehabilitation]], contributing to [[training]], [[education]], [[research]], and [[development]]). However, it is crucial to acknowledge the model's [[limitation]]s, encompassing knowledge constraints and the potential for [[erroneous response]]s, as well as ethical and legal considerations
((Zangrossi P, Martini M, Guerrini F, DE Bonis P, Spena G. [[Large language model]], AI and [[scientific research]]: why [[ChatGPT]] is only the beginning. J Neurosurg Sci. 2024 Jan 23. doi: 10.23736/S0390-5616.23.06171-4. Epub ahead of print. PMID: 38261307.)).