International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Volume 6 Issue 6
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Automated PowerPoint Presentation Generation from PDF Documents Using NLP and Machine Learning

Author(s) Kunal Suryawanshi, Aditya Gaikwad, Viraj Zuluk, Saif Kumbay, Manisha Mali
Country India
Abstract In this paper, we propose here an AI system to assist with the process of taking text files - mostly PDFs - and converting them into PowerPoint presentations. That way, the user would be allowed to upload their document and select keywords or topics as guidance in the extraction of relevant content. Using techniques such as NLP and summarization where libraries like Transformer 5 take the theses from keywords and highlight core information that links with those keywords efficiently. Our system, by including concepts of advanced machine learning approaches such as transformers and large language models, manages to produce clear and concise summarizations of every keyword. We then lay the foundation for our presentation slides. This process reduces the manual effort involving coming up with an informative and engaging presentation. This tool is most useful for teachers, practitioners, and students themselves because it saves time and effort in the work of extracting content and making summaries, thus facilitating making a presentation on a given topic. Python-implemented, this does scalable, efficient rewriting of vast texts into slide-based formats, which implies greater clarity and user-friendliness of the information provided across multiple contexts.
Keywords MACHINE LEARNING, LARGE LANGUAGE MODEL, PYTHON, TRANSFORMERS, FITZ.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-21
Cite This Automated PowerPoint Presentation Generation from PDF Documents Using NLP and Machine Learning - Kunal Suryawanshi, Aditya Gaikwad, Viraj Zuluk, Saif Kumbay, Manisha Mali - IJFMR Volume 6, Issue 6, November-December 2024.
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E-ISSN 2582-2160
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