Swarm Based Text Summarization

Binwahlan, Mohammed Salem; Salim, Naomie; Suanmali, Ladda

doi:10.1109/iacsit-sc.2009.61

Cited by 56 publications

(22 citation statements)

References 14 publications

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“…In designing the prototype, there are two activities involved, (i) determining the representation of PSO parameters and (ii) constructing the algorithm for the optimisation. PSO parameters include how to determine position's best (pBest), global's best position (gBest) and velocity [15].…”

Section: Signature Representationmentioning

confidence: 99%

Signature verification using Particle Swarm Optimisation

Manshor

Abdul-Rahman

Lin

et al. 2010

2010 International Conference of Soft Computing and Pattern Recognition

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Signature is a typical method in authenticating identities. It is commonly used in many official transactions as a symbol of approval and agreement between all parties involved in the specified transactions.Although many individuals can possess the same name in their birth certificates, their signature still differs from one person to another. Therefore, this study examined the degree with which a signature is considered similar and belonged to the same person. The study focused on the online signature that is represented by (x,y) coordinates. We applied Particle Swarm Optimisation (PSO) to identify the baseline feature and proceeded to verify whether two signatures are similar to each other. The result of the baseline feature was then compared to the truth baseline and PSO was shown to have 90% accuracy in detecting whether it was a straight, ascending or descending baseline. We then tested the verification algorithm on the same person and then on different persons with the results showing an 80% accuracy.

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Section: Signature Representationmentioning

confidence: 99%

Signature verification using Particle Swarm Optimisation

Manshor

Abdul-Rahman

Lin

et al. 2010

2010 International Conference of Soft Computing and Pattern Recognition

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“…Most of the techniques employed in single-document summarization are also employed in multi-document summarization. There exist some notable difficulties 8 . (1) The degree of redundancy of information available in a group of topically-related documents is significantly larger than the redundancy of information within a single document, since each document incorporates important concepts and also the required shared background.…”

Section: Introductionmentioning

confidence: 99%

Vertex Cover Algorithm Based Multi-document Summarization Using Information Content of Sentences

John¹,

Wilscy

2015

Procedia Computer Science

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In recent times, the requirement for the generation of multi-document summary has gained a lot of attention among researchers. Multi-document summarization systems focus on generating compressed form of the documents which maintains the pertinent features of the original documents. Mostly, text summarization techniques use the sentence extraction technique where the salient sentences in the multiple documents are selected and presented as a summary. In our proposed system, we have developed a sentence extraction based multi-document summarization system using the principle of vertex cover algorithm which automatically selects relevant sentences that cover the predominant concepts of the input documents. This frame work represents the documents as a weighted undirected graph with sentences as the vertices and the similarity between the sentences as the edge weight between the corresponding vertices in the graph. The experimental result on the DUC 2002 data sets demonstrates the effectiveness of the proposed method in document summarization

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“…Where: w i = The weight of feature i f i = The score of feature i Binwahlan et al (2009) proposed a text summarization model based on Particle Swarm Optimization (PSO) to determine the feature weights. Bossard and Rodrigues (2011) used genetic algorithm to approximate the best weight combination for their multi document summarizer.…”

Section: Proper Nounmentioning

confidence: 99%

A Review on Automatic Text Summarization Approaches

Kumar¹,

Goh²,

Basiron³

et al. 2016

Journal of Computer Science

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It has been more than 50 years since the initial investigation on automatic text summarization was started. Various techniques have been successfully used to extract the important contents from text document to represent document summary. In this study, we review some of the studies that have been conducted in this still-developing research area. It covers the basics of text summarization, the types of summarization, the methods that have been used and some areas in which text summarization has been applied. Furthermore, this paper also reviews the significant efforts which have been put in studies concerning sentence extraction, domain specific summarization and multi document summarization and provides the theoretical explanation and the fundamental concepts related to it. In addition, the advantages and limitations concerning the approaches commonly used for text summarization are also highlighted in this study.

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Swarm Based Text Summarization

Cited by 56 publications

References 14 publications

Signature verification using Particle Swarm Optimisation

Signature verification using Particle Swarm Optimisation

Vertex Cover Algorithm Based Multi-document Summarization Using Information Content of Sentences

A Review on Automatic Text Summarization Approaches

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