Multiple Text Document Summarization System using hybrid Summarization technique

Dave, Harsha; Jaswal, Shree

doi:10.1109/ngct.2015.7375231

Cited by 16 publications

(7 citation statements)

References 6 publications

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“…While evaluating data, there exist few words which are not valuable to text content. Thus, removing such redundant words is imperative, and this procedure is termed stop word removal [29]. The continual happening of words, like articles, conjunctions, and prepositions, such as the, is, a, an, and, when, but some high-frequency words are adapted as stop words.…”

Section: (I) Stop Word Removalmentioning

confidence: 99%

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WITHDRAWN: Rider Weed Deep Residual Network-Based Incremental Model for Text Classification Using Multidimensional Features and MapReduce Framework

Abdalla

2021

Preprint

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The increasing demand for information and rapid growth of big data has dramatically increased textual data. The amount of different kinds of data has led to the overloading of information. For obtaining useful text information, the classification of texts is considered an imperative task. This paper develops a technique for text classification in big data using the MapReduce model. The goal is to design a hybrid optimization algorithm for classifying the text. Here, the pre-processing is done with the steaming process and stop word removal. In addition, the Extraction of imperative features is performed wherein SentiWordNet features, contextual features, and thematic features are generated. Furthermore, the selection of optimal features is performed using Tanimoto similarity. The Tanimoto similarity method estimates the similarity between the features and selects the relevant features with higher feature selection accuracy. After that, a deep residual network is utilized for dynamic text classification. The Adam algorithm trains the deep residual network. In addition, the dynamic learning is performed with the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines Invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). The method mentioned above is solved under the MapReduce framework. The proposed RIWO-based deep residual network outperformed other techniques with the highest True positive rate (TPR) of 85%, True negative rate (TNR) of 94%, and accuracy of 88.7%.

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Section: (I) Stop Word Removalmentioning

confidence: 99%

“…Moreover, stemming is the procedure to retrieve information for describing the mechanism for removing redundant words to their root form and word stem. For instance, if a word starts from connections, connection, connecting, connected, the word is reduced as connect [29].…”

Section: (Ii) Stemmingmentioning

confidence: 99%

WITHDRAWN: Rider Weed Deep Residual Network-Based Incremental Model for Text Classification Using Multidimensional Features and MapReduce Framework

Abdalla

2021

Preprint

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“…Meronymy/Holonymy are called part-whole semantic relations. If the part of any whole represents the whole entity, it is known as its meronym (Dave & Jaswal, 2015). The analysis of nouns highlighted that 375 nouns out of 846 had the relation of meronymy e.g., ‫عقل‬ is a meronym of ‫.سوچ‬ When someone talks about ‫,عقل‬ the capability of thinking ‫سوچ‬ comes in our mind, so it can be said that ‫عقل‬ is a representation of ‫.سوچ‬ On the same lines ‫کھالڑی‬ is a meronym of ‫‪and‬کھيڈ‬ ‫ياری‬ is a meronym of ‫.يار‬ The wholes of which the meronyms are part called holonyms.…”

Section: Meronymy/ Holonymy (Part-whole Relation)mentioning

confidence: 99%

Analysis of Lecxico-Semantic Relations of Punjabi Shahmukhi Nouns: A Corpus Based Study

Hashmi¹,

Mahmood²,

Mahmood³

2019

IJEL

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The current study is an effort in the development of Lecxico-semantic relations among Punjabi Shahmukhi nouns. Semantic relations are those nets, which are found among nouns on the bases of word meanings. Development of semantic nets is taken as a key part while developing WordNet of any language. The WordNet of Punjabi Shahmukhi is not developed yet. The digital exposure and progress of Punjabi Shahmukhi is very slow in comparison to other languages of the world. The present study explores the kind of semantic relations found among the nouns of Punjabi Shahmukhi. WordNet organizes words on the basis of word meanings rather than word forms. WordNet of English includes four open class categories including; nouns, verbs, adverbs and adjectives, but present study is limited to the analysis of nouns. A corpus of 2 million words of Punjabi Shahmukhi was taken from different sources. Then, it was POS tagged and a list of 846 nouns was generated. Then, each noun was analyzed individually to develop its Lecxico-semantic relations including: synonymy, antonymy, meronyms, holonymy, hyponymy, hypernymy, singular, plural, masculine, feminine and HAS a part. The present research is significant and useful in the development of WordNet for Punjabi Shahmukhi. With the development of WordNet, it will be possible to run digital applications in Punjabi Shahmukhi including: machine translation, information retrieval, querying archive and report generation to automatic speech recognition, data mining, read aloud, robotics and many more. On the other hand, WordNet will help to maintain an international status for Punjabi Shahmukhi.

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“…When evaluating data, some words are not valuable to text content, and removing such redundant words is imperative. This procedure is termed stop word removal ( Dave & Jaswal, 2015 ). Certain words such as articles, conjunctions, and prepositions, continuously appear and are called stop words.…”

Section: Proposed Riwo-based Deep Residual Network For Text Classific...mentioning

confidence: 99%

“…Moreover, it is the procedure used to retrieve information for describing the mechanism of reducing redundant words to their root form and word stem. For instance, the words connections, connection, connecting, and connected are all reduced to connect ( Dave & Jaswal, 2015 ). …”

Section: Proposed Riwo-based Deep Residual Network For Text Classific...mentioning

confidence: 99%

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce

Abdalla

Ahmed

Zeebaree

et al. 2022

PeerJ Computer Science

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Increasing demands for information and the rapid growth of big data have dramatically increased the amount of textual data. In order to obtain useful text information, the classification of texts is considered an imperative task. Accordingly, this article will describe the development of a hybrid optimization algorithm for classifying text. Here, pre-processing was done using the stemming process and stop word removal. Additionally, we performed the extraction of imperative features and the selection of optimal features using the Tanimoto similarity, which estimates the similarity between features and selects the relevant features with higher feature selection accuracy. Following that, a deep residual network trained by the Adam algorithm was utilized for dynamic text classification. Dynamic learning was performed using the proposed Rider invasive weed optimization (RIWO)-based deep residual network along with fuzzy theory. The proposed RIWO algorithm combines invasive weed optimization (IWO) and the Rider optimization algorithm (ROA). These processes are carried out under the MapReduce framework. Our analysis revealed that the proposed RIWO-based deep residual network outperformed other techniques with the highest true positive rate (TPR) of 85%, true negative rate (TNR) of 94%, and accuracy of 88.7%.

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Multiple Text Document Summarization System using hybrid Summarization technique

Cited by 16 publications

References 6 publications

WITHDRAWN: Rider Weed Deep Residual Network-Based Incremental Model for Text Classification Using Multidimensional Features and MapReduce Framework

WITHDRAWN: Rider Weed Deep Residual Network-Based Incremental Model for Text Classification Using Multidimensional Features and MapReduce Framework

Analysis of Lecxico-Semantic Relations of Punjabi Shahmukhi Nouns: A Corpus Based Study

Rider weed deep residual network-based incremental model for text classification using multidimensional features and MapReduce

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