Authors: Swapnil Kuyate, Omdeep Jadhav, Pratik Jadhav
DOI Link: https://doi.org/10.22214/ijraset.2023.51481
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Our research paper presents an AI text summarization system that utilizes GPT, a powerful language model, to generate concise and meaningful summaries of lengthy text documents. The system consists of four modules: User, Android Application, GPT API, and GPT server. The User interacts with the system through the Android Application, which serves as the user interface. The GPT API acts as the intermediary between the Android Application and the GPT server, which hosts the GPT model and handles the text summarization process. The system employs state-of-the-art natural language processing techniques to generate summaries while preserving contextual coherence and salient information. The system\'s summarization capabilities are evaluated using metrics such as Rouge and F1 scores, demonstrating its effectiveness in capturing key information from different types of text documents. The system\'s integration with Android platforms provides convenient access for mobile users, making it suitable for applications such as news summarization, document summarization, and content curation. The modular architecture of the system allows for scalability and flexibility, enabling future enhancements and extensions. Our AI text summarization system utilizing GPT presents a promising approach for automatically summarizing large volumes of text, providing users with time-saving and meaningful summaries. The system has potential applications in various domains and can serve as a foundation for further research in the field of text summarization and natural language processing
Our research proposes an AI text summarization system using GPT, a powerful language model by OpenAI, to generate concise summaries of long texts. Leveraging deep learning techniques, including recurrent neural networks and transformer models, our system analyzes text data and generates coherent summaries. It incorporates a feedback mechanism for users to refine summaries, enhancing adaptability over time. Extensive experiments on diverse datasets show superior accuracy, coherence, and conciseness compared to existing methods. The implications are significant in domains like information retrieval, content curation, and accessibility, but ethical considerations must be addressed. In conclusion, our research aims to develop a GPT-based AI text summarization system with the potential for productivity enhancement, requiring further research on ethical concerns and real-world performance improvement.
The main objectives of the AI Text Summarization System is as follows:
III. SYSTEM ARCHITECTURE
IV. REQUIREMENTS (FUNCTIONAL AND NON-FUNCTIONAL)
A. Functional Requirements
B. Non-Functional Requirements
The AI text summarization system uses the Android application as the user interface, the GPT API as the intermediary for communication between the Android application and the GPT server, and the GPT server as the core component for text analysis and summarization using the GPT model.
The methodology of the AI text summarization system involves several steps to summarize text input provided by the user through an Android application, using the GPT (Generative Pre-trained Transformer) model via a GPT API and GPT server. Here's an overview of the methodology:
The methodology of the AI text summarization system leverages the GPT model's language processing capabilities, along with the Android application, GPT API, and GPT server, to automatically generate summaries of input text provided by the user, making it a useful tool for summarizing lengthy text documents or articles for quick understanding and information retrieval
VI. PROBLEM SOLVE
Aims to solve several problems related to text summarization using artificial intelligence. Here are some potential problems that our project addressed:
Overall, our project could help address some of the challenges associated with processing large volumes of text, making information more accessible and manageable for a wide range of users.
The We would like to express our sincere gratitude to all those who have supported us in this research endeavour. Firstly, we would like to thank our project guide P.U.Mandlik for their guidance and support throughout the project. Their valuable inputs and suggestions have greatly helped us in shaping our work. We are also grateful to the participants who generously gave their time and efforts to help us in collecting the necessary data for this research. We would also like to extend our thanks to the staff of the library and the IT department of our institution for their assistance in accessing and utilizing the resources necessary for this research. Lastly, we would like to thank our families and friends for their continuous encouragement and support throughout our academic journey.
The research paper presents an AI text summarization system that utilizes GPT, a powerful language model, to generate concise and meaningful summaries of lengthy text documents. The system comprises four modules: User, Android Application, GPT API, and GPT server, offering a modular and scalable framework. Through state-of-the-art natural language processing techniques, the system effectively generates summaries while maintaining contextual coherence and salient information. The system\'s integration with Android platforms allows for easy accessibility and potential applications in various domains. The system\'s summarization capabilities are evaluated using metrics such as Rouge and F1 scores, showcasing its effectiveness in capturing key information from different types of text documents. Future research directions could include further enhancements to the system\'s summarization capabilities, evaluation of different text types and languages, and incorporating user feedback for refinement. Overall, the AI text summarization system utilizing GPT presents a promising approach for automating the summarization of large volumes of text, with potential applications in diverse domains and contributions to the field of text summarization and natural language processing research.
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Copyright © 2023 Swapnil Kuyate, Omdeep Jadhav, Pratik Jadhav. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Paper Id : IJRASET51481
Publish Date : 2023-05-03
ISSN : 2321-9653
Publisher Name : IJRASET
DOI Link : Click Here