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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
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| Paper Title: | AI-Based Book Summary Generator Using NLP and Transformer Models |
| Authors Name: | M. NAGA KEERTHI , GEEDALA DINUSHA |
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IJRTI_205315
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| Published Paper Id: | IJRTI2507076 |
| Published In: | Volume 10 Issue 7, July-2025 |
| DOI: | |
| Abstract: | In the digital age, the volume of textual information available to users has grown exponentially, making it increasingly challenging to extract key insights quickly from large books and documents. This project presents an AI-based book summary generator that leverages cutting-edge Natural Language Processing (NLP) techniques and Transformer models to automate the summarization process. The system is designed to accept input in either .txt or .pdf format and produce a coherent and concise summary of the content. At its core, the project utilizes the T5 (Text-to-Text Transfer Transformer) model, a powerful pre-trained sequence-to-sequence transformer architecture developed by Google. The model is fine-tuned for summarization tasks, allowing it to interpret and condense complex textual data into meaningful short-form content. The program reads the input text, processes it to remove formatting noise, and splits it into manageable chunks if it exceeds model input limits (512 tokens). Each chunk is summarized individually, and the partial outputs are combined into a final summary. The system handles both short and long documents effectively, offering real-time performance for small files and scalable processing for larger texts. PDF parsing is managed using reliable Python libraries to ensure accurate text extraction, even from multi-page documents. Additionally, the generated summary can be saved locally for future reference. This project demonstrates the practical application of NLP in automating content understanding and reduction, with potential use cases in academic research, publishing, journalism, and personalized reading assistants. By minimizing the time required to grasp the essence of lengthy content, the system empowers users to make faster, more informed decisions. Future enhancements may include GUI integration, multilingual support, abstractive and extractive hybrid summarization, and summarization quality scoring metrics. |
| Keywords: | Natural Language Processing (NLP), Text Summarization, AI-Based Summarizer, Deep Learning, PDF/Text File Summarization, Sequence-to-Sequence Model, Document Summarizer, Machine Learning, Automated Book Summarization, PyTorch. |
| Cite Article: | "AI-Based Book Summary Generator Using NLP and Transformer Models", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 7, page no.a698-a706, July-2025, Available :http://www.ijrti.org/papers/IJRTI2507076.pdf |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2507076
Registration ID:205315
Published In: Volume 10 Issue 7, July-2025
DOI (Digital Object Identifier):
Page No: a698-a706 Country: ANKAPALLE, Andhra Pradesh, India Research Area: Master of Computer Application Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2507076 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2507076 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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