Data Summerization and Voice Assistant
Laxman Singh1, Ram Kumar Sharma2, Nikhil Saini3, Mrtyunjy Singh4
1Asst. Prof. Mr. Laxman Singh, Department of Computer Science, ABES Institute of Technology, Ghaziabad (U.P), India.
2Ram Kumar Sharma, Student, Department of Computer Science, ABES Institute of Technology, Ghaziabad (U.P), India.
3Nikhil Saini, Student, Department of Computer Science, ABES Institute of Technology, Ghaziabad (U.P), India.
4Mrtyunjy Singh, Student, Department of Computer Science, ABES Institute of Technology, Ghaziabad (U.P), India.
Manuscript received on 03 January 2024 | Revised Manuscript received on 08 February 2024 | Manuscript Accepted on 15 February 2024 | Manuscript published on 30 March 2024 | PP: 9-12 | Volume-4 Issue-2, February 2024 | Retrieval Number: 100.1/ijipr.C979613030224 | DOI: 10.54105/ijipr.C9796.04020224
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© The Authors. Published by Lattice Science Publication (LSP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This project focuses on data collection and writing, aiming to develop a framework for efficiently summarizing extensive knowledge available on the Internet. The proposed framework leverages morphological content and semantic information to sift through the vast amount of data online. The current information landscape is overwhelming, making it challenging for individuals to extract pertinent details quickly. The sheer volume of data on the Internet poses difficulties in searching for and assimilating relevant information from diverse sources. The solution lies in the development of an automatic writing system to address these challenges effectively. Summary summarization, a crucial aspect of this framework, involves identifying and condensing the most important and useful information from a given dataset. The goal is to create a concise version while retaining the full purpose of the original data entry. The significance of this approach becomes apparent in the face of the daunting task of making sense of big data, streamlining the process and facilitating efficient extraction of meaningful insights.
Keywords: Natural Language Processing (NLP), SBERT, Transformer, Hugging face, Tokens, Machine Learning, Summarization.
Scope of the Article: Communication of Visual Data