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IJMLC 2021 Vol.11(1): 21-27 ISSN: 2010-3700
DOI: 10.18178/ijmlc.2021.11.1.1009

Project Reporting Management System with AI based Assistive Features for Text Summarization

Jin Boon Benjamin Tan, Quan Chen, and Chai Kiat Yeo

Abstract—This paper details a proof-of-concept system called Project Reporting Management System (PRMS) to manage the project reporting process in a typical research centre where the process can be manual for many centres. In fact, it is general enough to be scaled up and deployed for a large department or scaled down for a smaller setup in any organization which needs a simple and efficient project progress reporting system but does not entail the kind of complexity and cost of commercial project management systems. Using a research centre scenario, the progress of the individual projects has to be tracked through the periodic submission of progress reports by the Principal Investigator (PI) of the project. The centre will need to consolidate these individual reports manually into a consolidated report and an executive summary for higher management. PRMS automates the tracking of individual projects and reporting deadlines, sends reminders and allows online submission of reports by the PIs. PRMS also incorporates assistive and automated features exploiting Machine Learning (ML) and Natural Language Processing (NLP) techniques to generate the consolidated report and rank sentences of verbose report for assistive text summarization to facilitate the manual process of producing an executive summary.

Index Terms—Web-based system, reporting process automation, machine learning, natural language processing, sentiment analysis, text summarization.

Jin Boon Benjamin Tan, Quan Chen and Chai Kiat Yeo are with School of Computer Science and Engineering, Nanyang Technological University, Singapore (e-mail: jtan346@e.ntu.edu.sg, qchen@ntu.edu.sg, asckyeo@ntu.edu.sg).


Cite: Jin Boon Benjamin Tan, Quan Chen, and Chai Kiat Yeo, "Project Reporting Management System with AI based  Assistive Features for Text Summarization," International Journal of Machine Learning and Computing vol. 11, no. 1, pp. 21-27, 2021.

Copyright © 2021 by the authors. 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 (CC BY 4.0).


General Information

  • ISSN: 2010-3700 (Online)
  • Abbreviated Title: Int. J. Mach. Learn. Comput.
  • Frequency: Bimonthly
  • DOI: 10.18178/IJMLC
  • Editor-in-Chief: Dr. Lin Huang
  • Executive Editor:  Ms. Cherry L. Chen
  • Abstracing/Indexing: Inspec (IET), Google Scholar, Crossref, ProQuest, Electronic Journals Library.
  • E-mail: ijmlc@ejournal.net

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