Text Summarization using Fuzzy Classification and Normal Distribution |
Author(s): |
| Anuja Patil , G.H.Raisoni College of engineering and management Wagholi ,pune; Sandip Patil, G.H.Raisoni College of engineering and Management wagholi,pune; Jenish Gajjar, G.H.Raisoni College of engineering and management,wagholi Pune; Suvarna Satkar, G.H.Raiosni College of engineering and management Wagholi,pune |
Keywords: |
| Fuzzy Classification, NLP, Feature Extraction, Gaussian Distribution |
Abstract |
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There has been extensive research that has been conducted in the field of Text Summarization, to decrease the time taken and increase its precision. The research has been going on for decades to solve this extremely complex problem with innumerable permutations and combinations with vast data and time constraints. This is one of the most crucial subjects for research as there is an immediate need for systems that can effectively summarizethe text in a stipulated amount of time. The most affected applications are Academic careers and courtrooms. The summary of documents is derived by utilizing Natural Language Processing and Machine Learning, with varying amount of accuracy. Therefore, to increase the accuracy of summaries, this article proposes a Fuzzy Classification model with Gaussian distribution to extract a semantically sound summary of a given input of multiple documents. |
Other Details |
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Paper ID: IJSRDV7I31214 Published in: Volume : 7, Issue : 3 Publication Date: 01/06/2019 Page(s): 1507-1511 |
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