Volume 20 No 22 (2022)
 Download PDF
HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labeling for Telugu text
Aluri. Lakshmi, Dr. D Latha
Accessing information from online has become difficult problem due to the internet's explosive rise in textual resources. User’s frequently sought for topic summaries from multiple sources to satisfy their informational demands. Single document abstractive summarization is significant role in Natural Language Processing (NLP) aiming to generate concise summary of source text from single document. Method of producing natural language summaries from text, keeping key points of the input document as such is a challenging task. In this research, hybrid optimization algorithm, namely proposed Hunter Sail Fish Optimizer (HSFO) is used leading to abstractive summarization. Here, acquired document is allowed for Semantic Role Labeling (SRL), at which Stanza tool is used for extraction of Predicate Argument Structures (PAS). Next to SRL, optimized features are generated by computation of semantic similarity using Wave-Hedges metrics. Moreover, semantic feature clustering of PAS is performed using Bayesian Fuzzy Clustering (BFC). Then, feature score is generated by utilization of HSFO for parameter selection, leading to abstractive summarization by Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN). Here, HSFO is devised by combining Hunter-Prey Optimizer (HPO) and Sail Fish Optimizer (SFO).The dataset used in this work is Telugu dataset, from which text document in the sentence form is acquired. Finally, performance of HSFO_LSTM-CNN is analyzed by using four performance measures, precision, recall, F-measure, and Rouge, which shows superior values of precision as 0.887, recall as 0.925, F-measure as 0.906, and rouge as 0.815.
Semantic Role Labeling (SRL), Predicate Argument Structures (PAS), Long Short-Term Memory (LSTM), Hunter-Prey Optimizer (HPO), Sail Fish Optimizer (SFO).
Copyright © Neuroquantology

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

Articles published in the Neuroquantology are available under Creative Commons Attribution Non-Commercial No Derivatives Licence (CC BY-NC-ND 4.0). Authors retain copyright in their work and grant IJECSE right of first publication under CC BY-NC-ND 4.0. Users have the right to read, download, copy, distribute, print, search, or link to the full texts of articles in this journal, and to use them for any other lawful purpose.