Unstructured Text Documents Summarization With Multi-Stage Clustering

Saeed, Muhammad Yahya; Awais, Muhammad; Talib, Ramzan; Younas, Muhammad

doi:10.1109/access.2020.3040506

Cited by 20 publications

(9 citation statements)

References 57 publications

(173 reference statements)

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“…The character is identified by examining its forms and comparing its features. A mouse-equipped framework for the perception of handwritten letters or images was proposed [18]. [19,20] presented a comparison of opinion summarization techniques and proposed an unsupervised algorithmic approach for opinion summarization that does not require annotated training data, adequately classifies the input text and achieves 96% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Dhankhar¹,

Gupta²,

Memon³

et al. 2022

Computer Systems Science and Engineering

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In today's digital era, the text may be in form of images. This research aims to deal with the problem by recognizing such text and utilizing the support vector machine (SVM). A lot of work has been done on the English language for handwritten character recognition but very less work on the under-resourced Hindi language. A method is developed for identifying Hindi language characters that use morphology, edge detection, histograms of oriented gradients (HOG), and SVM classes for summary creation. SVM rank employs the summary to extract essential phrases based on paragraph position, phrase position, numerical data, inverted comma, sentence length, and keywords features. The primary goal of the SVM optimization function is to reduce the number of features by eliminating unnecessary and redundant features. The second goal is to maintain or improve the classification system's performance. The experiment included news articles from various genres, such as Bollywood, politics, and sports. The proposed method's accuracy for Hindi character recognition is 96.97%, which is good compared with baseline approaches, and system-generated summaries are compared to human summaries. The evaluated results show a precision of 72% at a compression ratio of 50% and a precision of 60% at a compression ratio of 25%, in comparison to state-of-the-art methods, this is a decent result.

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Section: Literature Reviewmentioning

confidence: 99%

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Dhankhar¹,

Gupta²,

Memon³

et al. 2022

Computer Systems Science and Engineering

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“…In some cases, the individual would lose completely its position data that is not predictable due to the potential region in which it is situated. By resolving this problem, there is a need for modifying the parameters that are shown in Equation (19).…”

Section: Fisa-efo Algorithmmentioning

confidence: 99%

“…In 2020, Saeed et al 19 have examined the dynamic corpus creation model by combining extracted summarized text with metadata. The developed model has examined the interconnection of many unstructured documents.…”

Section: Literature Surveymentioning

confidence: 99%

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Intelligent deep learning‐based hierarchical clustering for unstructured text data

Jyothi

Lingamgunta

Eluri

2022

Concurrency and Computation

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Document clustering is a technique used to split the collection of textual content into clusters or groups. In modern days, generally, the spectral clustering is utilized in machine learning domain. By using a selection of text mining algorithms, the diverse features of unstructured content is captured for ensuing in rich descriptions. The main aim of this article is to enhance a novel unstructured text data clustering by a developed natural language processing technique. The proposed model will undergo three stages, namely, preprocessing, features extraction, and clustering. Initially, the unstructured data is preprocessed by the techniques such as punctuation and stop word removal, stemming, and tokenization. Then, the features are extracted by the word2vector using continuous Bag of Words model and term frequency-inverse document frequency. Then, unstructured features are performed by the hierarchical clustering using the optimizing the cut-off distance by the improved sensing area-based electric fish optimization (FISA-EFO). Tuned deep neural network is used for improving the clustering model, which is proposed by same algorithm. Thus, the results reveal that the model provides better clustering accuracy than other clustering techniques while handling the unstructured text data. K E Y W O R D S fitness improved sensing area-based electric fish optimization, hierarchical clustering, tuned deep neural network, unstructured text data clustering INTRODUCTIONGenerally, speech and text data are read by humans easily, but the machine learning and statistical modeling applications have some unstructured data and so, it is necessary to do some alterations in the coded input feature sets. 1 Data clustering is a technique used for splitting the data elements into many groups so that the elements in the same group have the highest similarity. Though, based on the cluster's attributes, there are diverse elements in other groups. The major aim of clustering techniques is to get centroids or cluster centers for characterizing the entire cluster. Few of the clustering techniques were performed and classified from different scenarios such as "density-based methods, grid-based methods, partitioning methods, and hierarchical methods." 2,3 Moreover, the data set is defined as categorical or numerical. The primary statistical features of numeric data are utilized for describing the distance function between data elements. The categorical data is imitated from the qualitative and quantitative data, and then the descriptions are attained from the counts. 4 By using a "textual virtual schematic model" (TVSM), the textual data are assigned in clusters and it follows three steps. Initially, the extraction of unstructured data is carried out from the data source, and then, it is changed into structured data. 5 After that, clustering is implemented on structured data. Finally, the comparison of documents is done for enhancing the performance of the query based on accuracy.The day today's life generates a huge amount of unstructured text...

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“…The clustering algorithms take an important role in this purpose, which group together the crime reports of similar crime types. There are many clustering methods [1,2] that can be used to cluster both structured and unstructured datasets. But research on report clustering over a long period of time has shown that it is neither an easy task nor a perfect solution yet.…”

Section: Introductionmentioning

confidence: 99%

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

et al. 2023

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Crime reports clustering is crucial for identifying and preventing criminal activities that frequently happened in society. In the proposed work, named entities in a report are recognized to extract the crime-related phrases and subsequently, the phrases are preprocessed by applying stopword removal and lemmatization operations. Next, the module of the universal encoder model, called the transformer, is applied to extract phrases of the report to get a sentence embedding for each associated sentence, aggregation of which finally provides the vector representation of that report. An innovative and efficient graph-based clustering algorithm consisting of splitting and merging operations has been proposed to get the cluster of crime reports. The proposed clustering algorithm generates overlapping clusters, which indicates the existence of reports of multiple crime types. The fuzzy theory has been used to provide a score to the report for expressing its membership into different clusters, and accordingly, the reports are labelled by multiple categories. The efficiency of the proposed method has been assessed by taking into account different datasets and comparing them with other state-of-the-art approaches with the help of various performance measure metrics.

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Unstructured Text Documents Summarization With Multi-Stage Clustering

Cited by 20 publications

References 57 publications

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Support Vector Machine Based Handwritten Hindi Character Recognition and Summarization

Intelligent deep learning‐based hierarchical clustering for unstructured text data

An Effective Fuzzy Clustering of Crime Reports Embedded by a Universal Sentence Encoder Model

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