Single document text summarization technique using optimal combination of cuckoo search algorithm, sentence scoring and sentiment score

Mandal, Shrabanti; Singh, Girish Kumar; Pal, Anita

doi:10.1007/s41870-021-00739-2

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…This strategy, meanwhile, occasionally favored copying from the introduction, losing the narrative structure in the process. Mandal, S., et al [6] used Cuckoo Search (CS) algorithm for single document text summarization. For text summarization, this algorithm had the best readability, coherence, and non -redundancy.…”

Section: Literature Assessmentmentioning

confidence: 99%

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HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

Aluri,

2023

Preprint

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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.

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Section: Literature Assessmentmentioning

confidence: 99%

“…Depending on the quantity of input or source documents, there is dual text summa rization like single-document as well as multi-document. Many strategies and methods, including single-document and multidocument summarization as well as extractive and abstractive summary, are used [6].…”

Section: Introductionmentioning

confidence: 99%

HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

Aluri,

2023

Preprint

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“…This is especially useful for businesses who need to quickly analyses customer feedback or financial data. Single document text summarization is important because it helps to reduce the amount of time that it takes to read and comprehend a document [2]. It also helps to provide a quick overview of the main points of the document.…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Nature-Inspired Metaheuristic Approaches for Single Document Text Summarization

Pravesh Patel

2024

IJRITCC

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In today era, day by day huge amount of data is collected on internet. The reading of text document or retrieving important information are time consuming process, so there is need for introducing effective text summarization technique. Text summarization, is the process of retrieving key information from lengthy document, its plays an essential role in information retrieval and content extraction. The paper we presented a comprehensive examination of nature-inspired metaheuristic algorithms, such as firefly, Cuckoo Search(CS) and Particle Swarm Optimization (PSO) to improve text summarization with an emphasis on single document datasets such as DUC-2001 and DUC-2002. The measurement of generated text summaries quality, generated summaries of datasets are compared with existing golden summaries and evaluated using ROUGE score. Our results show that nature-inspired metaheuristic-based approaches show potential for enhancing text summary of individual documents, metaheuristics methods improve summarizing effectiveness while offering a fresh viewpoint on how to handle the process within the confines of a single document dataset.

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Improved exponential cuckoo search method for sentiment analysis

Pandey

Kulhari²,

Mittal

et al. 2022

Multimed Tools Appl

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Single document text summarization technique using optimal combination of cuckoo search algorithm, sentence scoring and sentiment score

Cited by 5 publications

References 25 publications

HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

HSFO: Hunter Sail Fish Optimizer enabled deep learning for single document abstractive summarization based on semantic role labelling for Telugu text

Performance Evaluation of Nature-Inspired Metaheuristic Approaches for Single Document Text Summarization

Improved exponential cuckoo search method for sentiment analysis

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