Evaluate the accurate prediction of text summarization using novel long short term memory algorithm in comparison with random forest

The goal of this proposed research is to find an alternative to the Random Forest method for text summarising that is both more accurate and more efficient in reducing lengthy texts to a manageable size. In order to summarise the text using a sample size of 63459 utilisingClincalc with a G power of...

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Main Authors: Rupesh, Kollaikal, Christy, S.
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Algorithms
Confidence intervals
Forest management
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description The goal of this proposed research is to find an alternative to the Random Forest method for text summarising that is both more accurate and more efficient in reducing lengthy texts to a manageable size. In order to summarise the text using a sample size of 63459 utilisingClincalc with a G power of 0.8, alpha of 0.05, and a 95% confidence level, we employed two algorithms: Novel Long Short Term Memory (N=10) and Random Forest (N=10). The level of correctness in the text summary is used to evaluate them. Results and Discussion: In the dataset, we find that Novel Long Short Term Memory achieves an accuracy of 94.45% and Random Forest achieves 73.57% when it comes to summarising the text. According to the Independent Sample T-Test (p
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subjects Accuracy
Algorithms
Confidence intervals
Forest management
title Evaluate the accurate prediction of text summarization using novel long short term memory algorithm in comparison with random forest
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