17 results on '"TEXT summarization"'
Search Results
2. ChatGPT and BCI-VR: a new integrated diagnostic and therapeutic perspective for the accurate diagnosis and personalized treatment of mild cognitive impairment.
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Yiduo ao, Hasan, W. Z. W., Wenlong Jiao, Xianling Dong, Ramli, H. R., Norsahperi, N. M. H., and Dong Wen
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MILD cognitive impairment ,CHATGPT ,LANGUAGE models ,TEXT summarization ,DIAGNOSIS ,AUTOMOTIVE navigation systems ,PLASMONICS ,CEREBROSPINAL fluid examination - Abstract
This document provides a list of references to scientific articles and papers related to topics such as Alzheimer's disease, cognitive decline, virtual reality, and machine learning. The articles cover a range of subjects including biomarkers for Alzheimer's disease diagnosis, cognitive training interventions, the use of virtual reality in assessing cognitive impairment, and the application of machine learning in healthcare. These references can be helpful for library patrons conducting research on these specific topics. [Extracted from the article]
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- 2024
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3. Chatgpt in and for second language acquisition: A call for systematic research.
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Han, ZhaoHong
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CHATGPT ,GENERATIVE pre-trained transformers ,LANGUAGE models ,SECOND language acquisition ,COMPUTER assisted language instruction ,TEXT summarization - Abstract
The article discusses the use of Artificial Intelligence (A.I.) tools, specifically chatbots, in second language acquisition. It highlights the potential benefits and concerns associated with the use of A.I. in language learning. The article calls for systematic research to explore the capabilities of A.I. language models, such as ChatGPT, and their impact on language learning. It suggests three strands of research: examining the language model itself, investigating learner interactions with the model, and exploring the effects of the model on learners. The article emphasizes the need to connect this research with existing studies in Instructed Second Language Acquisition (ISLA) to enhance our understanding of language learning conditions. [Extracted from the article]
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- 2024
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4. QUICK REVIEW OF PEDAGOGICAL EXPERIENCES USING GPT-3 IN EDUCATION.
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Manuel Prieto-Andreu, Joel and Labisa-Palmeira, Antonio
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GENERATIVE pre-trained transformers ,LANGUAGE models ,TEXT summarization ,CHATBOTS ,COMPUTER science ,ARTIFICIAL intelligence - Abstract
GPT-3 is a neuronal language model that performs tasks such as classification, question-answering and text summarization. Although chatbots like BlenderBot-3 work well in a conversational sense, and GPT-3 can assist experts in evaluating questions, they are quantifiably worse than real teachers in several pedagogical dimensions. We present the first systematic literature review that analyzes the main contributions and uses of GPT-3 in the field of education. The protocols suggested in the PRISMA 2020 statement were followed for the drafting of the review. According to the results, 34 significant productions were identified through a systematic search in ISI Web of Science, SCOPUS and Google Scholar. GPT-3 has been considered in the academic, ethical and medical fields, in humanities and in computer science, in the formulation of questions and answers, and through cooperative educational dialogs. GPT-3 has been proven to have valuable applications in education, such as the automation of routine tasks, in making quick diagnoses of the students' weaknesses and in the automatic generation of questions, but it still faces challenges and limitations that require additional investigation. We discuss the educational possibilities and the limitations to the use of GPT-3. [ABSTRACT FROM AUTHOR]
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- 2024
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5. An integrative decision-making framework to guide policies on regulating ChatGPT usage.
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Bukar, Umar Ali, Sayeed, Md Shohel, Razak, Siti Fatimah Abdul, Yogarayan, Sumendra, and Amodu, Oluwatosin Ahmed
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GENERATIVE artificial intelligence ,CHATGPT ,TEXT summarization ,ARTIFICIAL intelligence ,EDUCATION ethics ,DIGITAL divide - Abstract
Generative artificial intelligence has created a moment in history where human beings have begin to closely interact with artificial intelligence (AI) tools, putting policymakers in a position to restrict or legislate such tools. One particular example of such a tool is ChatGPT which is the first and world's most popular multipurpose generative AI tool. This study aims to put forward a policy-making framework of generative artificial intelligence based on the risk, reward, and resilience framework. A systematic search was conducted, by using carefully chosen keywords, excluding non-English content, conference articles, book chapters, and editorials. Published research were filtered based on their relevance to ChatGPT ethics, yielding a total of 41 articles. Key elements surrounding ChatGPT concerns and motivations were systematically deduced and classified under the risk, reward, and resilience categories to serve as ingredients for the proposed decision-making framework. The decisionmaking process and rules were developed as a primer to help policymakers navigate decision-making conundrums. Then, the framework was practically tailored towards some of the concerns surrounding ChatGPT in the context of higher education. In the case of the interconnection between risk and reward, the findings show that providing students with access to ChatGPT presents an opportunity for increased efficiency in tasks such as text summarization and workload reduction. However, this exposes them to risks such as plagiarism and cheating. Similarly, pursuing certain opportunities such as accessing vast amounts of information, can lead to rewards, but it also introduces risks like misinformation and copyright issues. Likewise, focusing on specific capabilities of ChatGPT, such as developing tools to detect plagiarism and misinformation, may enhance resilience in some areas (e.g., academic integrity). However, it may also create vulnerabilities in other domains, such as the digital divide, educational equity, and job losses. Furthermore, the finding indicates secondorder effects of legislation regarding ChatGPT which have implications both positively and negatively. One potential effect is a decrease in rewards due to the limitations imposed by the legislation, which may hinder individuals from fully capitalizing on the opportunities provided by ChatGPT. Hence, the risk, reward, and resilience framework provides a comprehensive and flexible decision-making model that allows policymakers and in this use case, higher education institutions to navigate the complexities and trade-offs associated with ChatGPT, which have theoretical and practical implications for the future. [ABSTRACT FROM AUTHOR]
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- 2024
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6. Opportunities and challenges for ChatGPT and large language models in biomedicine and health.
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Tian, Shubo, Jin, Qiao, Yeganova, Lana, Lai, Po-Ting, Zhu, Qingqing, Chen, Xiuying, Yang, Yifan, Chen, Qingyu, Kim, Won, Comeau, Donald C, Islamaj, Rezarta, Kapoor, Aadit, Gao, Xin, and Lu, Zhiyong
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LANGUAGE models , *CHATGPT , *TEXT summarization , *GENERATIVE artificial intelligence , *DATA privacy , *DATA mining - Abstract
ChatGPT has drawn considerable attention from both the general public and domain experts with its remarkable text generation capabilities. This has subsequently led to the emergence of diverse applications in the field of biomedicine and health. In this work, we examine the diverse applications of large language models (LLMs), such as ChatGPT, in biomedicine and health. Specifically, we explore the areas of biomedical information retrieval, question answering, medical text summarization, information extraction and medical education and investigate whether LLMs possess the transformative power to revolutionize these tasks or whether the distinct complexities of biomedical domain presents unique challenges. Following an extensive literature survey, we find that significant advances have been made in the field of text generation tasks, surpassing the previous state-of-the-art methods. For other applications, the advances have been modest. Overall, LLMs have not yet revolutionized biomedicine, but recent rapid progress indicates that such methods hold great potential to provide valuable means for accelerating discovery and improving health. We also find that the use of LLMs, like ChatGPT, in the fields of biomedicine and health entails various risks and challenges, including fabricated information in its generated responses, as well as legal and privacy concerns associated with sensitive patient data. We believe this survey can provide a comprehensive and timely overview to biomedical researchers and healthcare practitioners on the opportunities and challenges associated with using ChatGPT and other LLMs for transforming biomedicine and health. [ABSTRACT FROM AUTHOR]
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- 2024
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7. A Comprehensive Study of ChatGPT: Advancements, Limitations, and Ethical Considerations in Natural Language Processing and Cybersecurity.
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Alawida, Moatsum, Mejri, Sami, Mehmood, Abid, Chikhaoui, Belkacem, and Isaac Abiodun, Oludare
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CHATGPT , *NATURAL language processing , *LANGUAGE models , *TEXT summarization , *INTERNET security , *HAZARD mitigation - Abstract
This paper presents an in-depth study of ChatGPT, a state-of-the-art language model that is revolutionizing generative text. We provide a comprehensive analysis of its architecture, training data, and evaluation metrics and explore its advancements and enhancements over time. Additionally, we examine the capabilities and limitations of ChatGPT in natural language processing (NLP) tasks, including language translation, text summarization, and dialogue generation. Furthermore, we compare ChatGPT to other language generation models and discuss its applicability in various tasks. Our study also addresses the ethical and privacy considerations associated with ChatGPT and provides insights into mitigation strategies. Moreover, we investigate the role of ChatGPT in cyberattacks, highlighting potential security risks. Lastly, we showcase the diverse applications of ChatGPT in different industries and evaluate its performance across languages and domains. This paper offers a comprehensive exploration of ChatGPT's impact on the NLP field. [ABSTRACT FROM AUTHOR]
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- 2023
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8. What is ChatGPT and how will it impact project management?
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Reid, Lawrence
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PROJECT managers ,CHATGPT ,PROJECT management ,LANGUAGE models ,TEXT summarization - Published
- 2023
9. Enhancing Abstractive Summarization with Extracted Knowledge Graphs and Multi-Source Transformers.
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Chen, Tong, Wang, Xuewei, Yue, Tianwei, Bai, Xiaoyu, Le, Cindy X., and Wang, Wenping
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TEXT summarization ,KNOWLEDGE graphs ,LANGUAGE models ,CHATGPT - Abstract
As the popularity of large language models (LLMs) has risen over the course of the last year, led by GPT-3/4 and especially its productization as ChatGPT, we have witnessed the extensive application of LLMs to text summarization. However, LLMs do not intrinsically have the power to verify the correctness of the information they supply and generate. This research introduces a novel approach to abstractive summarization, aiming to address the limitations of LLMs in that they struggle to understand the truth. The proposed method leverages extracted knowledge graph information and structured semantics as a guide for summarization. Building upon BART, one of the state-of-the-art sequence-to-sequence pre-trained LLMs, multi-source transformer modules are developed as an encoder, which are capable of processing textual and graphical inputs. Decoding is performed based on this enriched encoding to enhance the summary quality. The Wiki-Sum dataset, derived from Wikipedia text dumps, is introduced for evaluation purposes. Comparative experiments with baseline models demonstrate the strengths of the proposed approach in generating informative and relevant summaries. We conclude by presenting our insights into utilizing LLMs with graph external information, which will become a powerful aid towards the goal of factually correct and verified LLMs. [ABSTRACT FROM AUTHOR]
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- 2023
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10. A Mathematical Interpretation of Autoregressive Generative Pre-Trained Transformer and Self-Supervised Learning.
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Lee, Minhyeok
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LANGUAGE models , *TEXT summarization , *QUESTION answering systems , *NATURAL language processing , *INVERSE functions , *AUTOREGRESSIVE models , *NATURAL languages - Abstract
In this paper, we present a rigorous mathematical examination of generative pre-trained transformer (GPT) models and their autoregressive self-supervised learning mechanisms. We begin by defining natural language space and knowledge space, which are two key concepts for understanding the dimensionality reduction process in GPT-based large language models (LLMs). By exploring projection functions and their inverses, we establish a framework for analyzing the language generation capabilities of these models. We then investigate the GPT representation space, examining its implications for the models' approximation properties. Finally, we discuss the limitations and challenges of GPT models and their learning mechanisms, considering trade-offs between complexity and generalization, as well as the implications of incomplete inverse projection functions. Our findings demonstrate that GPT models possess the capability to encode knowledge into low-dimensional vectors through their autoregressive self-supervised learning mechanism. This comprehensive analysis provides a solid mathematical foundation for future advancements in GPT-based LLMs, promising advancements in natural language processing tasks such as language translation, text summarization, and question answering due to improved understanding and optimization of model training and performance. [ABSTRACT FROM AUTHOR]
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- 2023
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11. Grapharizer: A Graph-Based Technique for Extractive Multi-Document Summarization.
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Jalil, Zakia, Nasir, Muhammad, Alazab, Moutaz, Nasir, Jamal, Amjad, Tehmina, and Alqammaz, Abdullah
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TEXT summarization ,MACHINE learning ,BIG data - Abstract
Featured Application: A graph-based technique tested on a benchmark dataset and augmented by machine learning techniques to provide a concise, informative, and grammatically correct summary. In the age of big data, there is increasing growth of data on the Internet. It becomes frustrating for users to locate the desired data. Therefore, text summarization emerges as a solution to this problem. It summarizes and presents the users with the gist of the provided documents. However, summarizer systems face challenges, such as poor grammaticality, missing important information, and redundancy, particularly in multi-document summarization. This study involves the development of a graph-based extractive generic MDS technique, named Grapharizer (GRAPH-based summARIZER), focusing on resolving these challenges. Grapharizer addresses the grammaticality problems of the summary using lemmatization during pre-processing. Furthermore, synonym mapping, multi-word expression mapping, and anaphora and cataphora resolution, contribute positively to improving the grammaticality of the generated summary. Challenges, such as redundancy and proper coverage of all topics, are dealt with to achieve informativity and representativeness. Grapharizer is a novel approach which can also be used in combination with different machine learning models. The system was tested on DUC 2004 and Recent News Article datasets against various state-of-the-art techniques. Use of Grapharizer with machine learning increased accuracy by up to 23.05% compared with different baseline techniques on ROUGE scores. Expert evaluation of the proposed system indicated the accuracy to be more than 55%. [ABSTRACT FROM AUTHOR]
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- 2023
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12. Utilizing Large Language Models in Ophthalmology: The Current Landscape and Challenges.
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Chotcomwongse, Peranut, Ruamviboonsuk, Paisan, and Grzybowski, Andrzej
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LANGUAGE models , *NATURAL language processing , *TEXT summarization , *ARTIFICIAL intelligence , *CHATGPT - Abstract
A large language model (LLM) is an artificial intelligence (AI) model that uses natural language processing (NLP) to understand, interpret, and generate human-like language responses from unstructured text input. Its real-time response capabilities and eloquent dialogue enhance the interactive user experience in human–AI communication like never before. By gathering several sources on the internet, LLM chatbots can interact and respond to a wide range of queries, including problem solving, text summarization, and creating informative notes. Since ophthalmology is one of the medical fields integrating image analysis, telemedicine, AI, and other technologies, LLMs are likely to play an important role in eye care in the near future. This review summarizes the performance and potential applicability of LLMs in ophthalmology according to currently available publications. [ABSTRACT FROM AUTHOR]
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- 2024
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13. JPMorgan introduces an internal chatbot to serve as an AI-based research analyst.
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GENERATIVE artificial intelligence ,TEXT summarization ,LANGUAGE models ,ARTIFICIAL intelligence ,CHATGPT ,CHATBOTS - Abstract
JPMorgan Chase has implemented a generative artificial intelligence (AI) product called OpenAI's ChatGPT to serve as a research analyst. The bank has provided its asset and wealth management division with access to a language model known as LLM Suite, which helps with writing, idea generation, and document summarization. This move follows a similar initiative by rival firm Morgan Stanley, which launched a chatbot powered by GenAI and OpenAI to provide financial advisors with quick access to the company's intellectual capital. Currently, 50,000 JPMorgan employees have access to the AI chatbot. [Extracted from the article]
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- 2024
14. Text summarization with ChatGPT for drug labeling documents.
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Ying, Lan, Liu, Zhichao, Fang, Hong, Kusko, Rebecca, Wu, Leihong, Harris, Stephen, and Tong, Weida
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TEXT summarization , *CHATGPT , *LANGUAGE models , *DRUG labeling , *DRUG discovery , *NATURAL language processing - Abstract
• Text summarization in scientific research is crucial but time consuming and labor intensive, and it requires specific expertise; here, NLP is playing a role. • ChatGPT's drug information summarization was evaluated against human experts using >14 000 records from the FDA Labeling document for the first time. • ChatGPT reliably generates summaries closely resembling those of human experts, particularly in the context of summarizing drug safety information from labeling documents. • ChatGPT and its derivatives are valuable in drug discovery, improving drug labeling access and supporting FDA review processes. Text summarization is crucial in scientific research, drug discovery and development, regulatory review, and more. This task demands domain expertise, language proficiency, semantic prowess, and conceptual skill. The recent advent of large language models (LLMs), such as ChatGPT, offers unprecedented opportunities to automate this process. We compared ChatGPT-generated summaries with those produced by human experts using FDA drug labeling documents. The labeling contains summaries of key labeling sections, making them an ideal human benchmark to evaluate ChatGPT's summarization capabilities. Analyzing >14 000 summaries, we observed that ChatGPT-generated summaries closely resembled those generated by human experts. Importantly, ChatGPT exhibited even greater similarity when summarizing drug safety information. These findings highlight ChatGPT's potential to accelerate work in critical areas, including drug safety. [ABSTRACT FROM AUTHOR]
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- 2024
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15. Topic model for long document extractive summarization with sentence-level features and dynamic memory unit.
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Han, Chunlong, Feng, Jianzhou, and Qi, Haotian
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TEXT summarization , *LANGUAGE models , *CHATGPT , *MEMORY - Abstract
The Transformer-based summarization models rely solely on the attention mechanism for document encoding, making it difficult to accurately capture long-range dependencies in long documents due to the presence of attention redundancy. To address this issue, we propose an extractive summarization framework guided by a topic model (TopicSum) that utilizes a heterogeneous graph neural network to leverage the topic information as document-level features during the sentence selection process, thereby capturing the long-range dependencies among sentences. The sentence-level features in this topic model align with the basic unit of the extractive summarization task. Additionally, a memory mechanism is employed to dynamically store and update the memory module, reducing the potential of repetitive information guiding sentence selection. We evaluated the model on three large document datasets, namely Pubmed, arXiv, and GovReport, and achieved significantly higher Rouge scores than previous works, including extractive and abstractive models. Furthermore, our experiments demonstrate that recent highly regarded large language models such as ChatGPT are insufficient to handle the long document summarization task directly. The proposed approach in this paper exhibits sufficient competitiveness in terms of both generation quality and deployment conditions. [ABSTRACT FROM AUTHOR]
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- 2024
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16. COPILOT GETS GPT4 TURBO.
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C. L.
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TEXT summarization ,CHATGPT ,LANGUAGE models ,ARTIFICIAL intelligence ,VIDEOS ,TEXT recognition - Abstract
Microsoft is upgrading its AI engine, Copilot, to GPT-4 Turbo, the latest version of OpenAI's ChatGPT engine. This new version has been trained on data up to April 2023 and has a quadrupled query size, allowing users to input larger amounts of text for summarization. Copilot now includes a code interpreter feature for programmers, the ability to summarize YouTube videos, and the capability to rewrite text without changing the content. Microsoft has invested $10 billion in OpenAI and holds a 49 percent stake, with recent tensions over control of the organization. [Extracted from the article]
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- 2024
17. U.S. Food and Drug Administration (FDA) Reports Findings in Food and Farming (Leveraging GPT-4 for Food Effect Summarization to Enhance Product-Specific Guidance Development via Iterative Prompting).
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LANGUAGE models ,AGRICULTURE ,TEXT summarization ,CHATGPT ,GENERIC products - Abstract
A recent report from the U.S. Food and Drug Administration (FDA) discusses the development of automated methods for generating food effect summaries. These summaries are important for providing guidance to the pharmaceutical industry on fasting and fed bioequivalence studies. The report introduces an approach called iterative prompting, which uses large language models like GPT-4 to improve the accuracy and efficiency of automated text summarization. The study found that GPT-4 performed better than ChatGPT in generating summaries that were factually consistent with the reference summaries. The researchers believe that GPT-4 has the potential to improve the efficiency of the guidance development process and promote the development of generic drug products. [Extracted from the article]
- Published
- 2023
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