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All Journal Journal of Innovation Information Technology and Application (JINITA)
Aryaan Shaikh
Vishwakarma Institute of Information Technology, Pune
Computer Science & IT Decision Sciences, Operations Research & Management Engineering

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Autocomplete recommendation plugin and Summarizing Text using Natural Language Processing Aryaan Shaikh; Nikita Newalkar; Sakshi Gaikwad; Namrata Kadav; Chaitali Shewale
Journal of Innovation Information Technology and Application (JINITA) Vol 5 No 2 (2023): JINITA, December 2023
Publisher : Politeknik Negeri Cilacap

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35970/jinita.v5i2.1912

Abstract

Expert-caliber documents, reports, letters, and resumes can be easily developed using Microsoft Office. Microsoft Office offers capabilities such as grammar check, text and font checking & formatting, HTML compatibility, advanced page layout, image support, and more in contrast to a plain text editor, however, it does not have the autocomplete abbreviations feature. The paper proposes an Autocomplete abbreviation Recommendation System that will integrate the benefits of getting automatic suggestions of either full forms, abbreviations, or both by clicking on the option that is being suggested. This will provide more flexibility to the user using existing Microsoft Office platforms. To create this feature, we have examined the JavaScript JQuery functions to implement a basic autocomplete feature. Information overloading is also one of the most important problems brought on by the Internet's explosive expansion. Massive quantities of text are difficult for people to manually summarise. Thus, there is now a greater need for summarizers that are more sophisticated and potent. Hence, Python's packages, methods, and NLP are used in this work to implement Text Summarization. By using this technique, the phrase's overall meaning is enhanced and the reader's comprehension is enhanced.
Co-Authors Chaitali Shewale Namrata Kadav Nikita Newalkar Sakshi Gaikwad