Hybrid Document Summarization using NLP

Authors

  • R Chandramma Dept. of Computer Science and Engineering Vivekananda Institute of Technology Bangalore, India
  • Pandurangi NP Dept. of Computer Science and Engineering Vivekananda Institute of Technology Bangalore, India
  • Jamadagni SV Dept. of Computer Science and Engineering Vivekananda Institute of Technology Bangalore, India
  • Nikhil Chandran Dept. of Computer Science and Engineering Vivekananda Institute of Technology Bangalore, India
  • Ahmed MAT Dept. of Computer Science and Engineering Vivekananda Institute of Technology Bangalore, India

Keywords:

Document Summarization, Natural Language Processing, Word Net, NLTK

Abstract

Hybrid Document Summarization is the technique by which the huge parts of content are retrieved. The Hybrid Document Summarization plays out the summarization task by unsupervised learning system. The significance of a sentence in info content is assessed by the assistance of 3 algorithms. As an online semantic lexicon WordNet is utilized. Word Sense Disambiguation (WSD) is a critical and testing system in the territory of characteristic dialect handling (NLP). A specific word may have distinctive significance in various setting. So, the principle task of word sense disambiguation is to decide the right feeling of a word utilized as a part of a specific setting. To begin with, Document Summarization assesses the weights, keyword and parts of speech of the considerable number of sentences of a content independently utilizing the algorithms and orchestrates them in diminishing request as indicated by their weights. Next, as indicated by the given level of rundown, a specific number of sentences are chosen from that requested rundown.

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Published

2025-11-26

How to Cite

[1]
R. Chandramma, N. Pandurangi, S. Jamadagni, N. C., and M. A. T. Ahmed, “Hybrid Document Summarization using NLP”, Int. J. Comp. Sci. Eng., vol. 7, no. 15, pp. 243–247, Nov. 2025.

Issue

Vol. 7 No. 15 (2019): IJCSE Special Issue May Edition

Section

Research Article

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