Abstract :

With The Exponential Rise Of Digital Documentation, Information Overload Has Become A Critical Challenge For Students, Researchers, And Professionals. Traditional Text Summarization Tools Often Generate Unstructured Summaries That Are Difficult To Comprehend Quickly. The Mind Map-Based PDF Summarizer Addresses This Issue By Combining The Efficiency Of Natural Language Processing (NLP) With The Clarity Of Mind Mapping. This Approach Not Only Reduces The Time Spent Reading Long Documents But Also Enhances Knowledge Retention By Presenting Content In A Visual And Hierarchical Format. The Proposed Model Leverages NLP Pipelines, Keyword Extraction Algorithms, And Automatic Mind Map Generation Techniques To Deliver Meaningful Summaries In A Humanfriendly Format. The Results Demonstrate That Mind Map Visualization Improves User Engagement, Comprehension, And Recall Accuracy Compared To Linear Summaries. Mind Map Summaries Start From A Central Topic And Branch Outward Into Major Themes, Subtopics, And Supporting Details, Forming A Non-linear Diagram Of The Content. Each Node Represents A Concept Or Keyword, And Links Between Nodes Show How Ideas Relate, Similar To Concept Maps Used For Summarization And Knowledge Organization. Using Mind Maps For Summarizing Helps Distinguish Levels Of Ideas, Remove Redundancy, And Highlight Only The Most Important Information From A Source. Studies In Education Report That Mind Mapping Techniques Improve Students’ Summarizing Skills, Learning Outcomes, And Motivation By Making Summarization More Visual And Interactive

Published:

07-1-2026

Issue:

Vol. 26 No. 1 (2026)

Page Nos:

164-168

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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