Graph-based biomedical text summarization: An itemset mining and sentence clustering approach

Azadani, Mozhgan Nasr; Ghadiri, Nasser; Davoodijam, Ensieh

doi:10.1016/j.jbi.2018.06.005

Cited by 61 publications

(29 citation statements)

References 35 publications

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“…There exist various forms of text summarizations, and their selection depends on the processing requirements [4]. Automatic text assessment and corpus processing give better results with reduced cost and time [4], [5]. This section shows the relevance of the current technique with other studies over corpus processing.…”

Section: Discussionmentioning

confidence: 99%

“…The data cleansing removes elements like exclusive characters, punctuation, and tags. All such items are useless for the underlying text processing and only result as unnecessary noise [5], [8], [21]. However, there exists no fixed rule to categorically declare the specific noise in any type of textual data.…”

Section: A Text Pre-processingmentioning

confidence: 99%

“…Less efficient grouping ultimately increases the cost and effort involved in the information retrieval process. Text mining experts determine the processing cost & time by the size and information diversity of the underlying corpus [4], [5]. Text processing efficiency improves by consuming less time with a small related corpus.…”

Section: Introductionmentioning

confidence: 99%

“…However, still, it has vast application over those documents which contain relatively short unstructured text. It is very much suitable for tasks such as headlines, various documents title, website or research paper keywords [5], [21], sentence compression [13], [19], [24], and sentence fusion [11], [17]. The keyword selection has many concerns and constraints, and as per literature, one cannot guarantee it as stand-alone the comprehensive text representation.…”

Section: Introductionmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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In natural language processing, text summarization is an important application used to extract desired information by reducing large text. Existing studies use keyword-based algorithms for grouping text, which do not give the documents' actual theme. Our proposed dynamic corpus creation mechanism combines metadata with summarized extracted text. The proposed approach analyzes the mesh of multiple unstructured documents and generates a linked set of multiple weighted nodes by applying multistage Clustering. We have generated adjacency graphs to link the clusters of various collections of documents. This approach comprises of ten steps: pre-processing, making multiple corpuses, first stage clustering, creating sub-corpuses, interlinking sub-corpuses, creating page rank keyword dictionary of each sub-corpus, second stage clustering, path creation among clusters of sub-corpuses, text processing by forward and backward propagation for results generation. The outcome of this technique consists of interlinked subcorpuses through clusters. We have applied our approach to a News dataset, and this interlinked corpus processing follows step by step clustering to search the most relevant parts of the corpus with less cost, time, and improve content detection. We have applied six different metadata processing combinations over multiple text queries to compare results during our experimentation. The comparison results of text satisfaction show that Page-Rank keywords give 38% related text, single-stage Clustering gives 46%, twostage Clustering gives 54%, and the proposed technique gives 67% associated text. Furthermore, this approach covers/searches the relevant data with a range of most to less relevant content. It provides the systematic query-relevant corpus processing mechanism, which automatically selects the most relevant subcorpus through dynamic path selection. We used the SHAP model to evaluate the proposed technique, and our evaluation results proved that the proposed mechanism improved text processing. Moreover, combining text summarization features, shown satisfactory results compared to the summaries generated by general models of abstractive & extractive summarization.

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Section: Discussionmentioning

confidence: 99%

Section: A Text Pre-processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

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“…Azadani et al [24 ] used the UMLS to construct a concept‐based model of the source document and map it to the concepts. It discovered frequent itemsets to take the correlations among multiple ideas into account.…”

Section: Backgrounds and Related Workmentioning

confidence: 99%

Automatic summarising of user stories in order to be reused in future similar projects

et al. 2020

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User stories play an important role in agile development systems. In this study, a method of summarising user stories is proposed to reuse them in the future. To enhance the results, quality improvement should be made on user stories. It would help developers build better results, and it may also lead to omitting some essential information. To avoid such issues, user stories are duplicated in two exact similar groups, and quality improvement is made on one set while the other set remains unattained. With the help of a modified bag of words and a verb parser, a collection of keywords and key verbs are extracted for both groups. Afterwards, automatic user stories are made, and then an expert improves them. Next, some experts choose between the results and select the better ones. The result is evaluated by applying different experiments on the framework and prototype implementation on 14 data sets of a user story from industry and a fake data set from Duke University. The result showed 97% of micro F ‐measure and 93% of macro F ‐measure, which are promising. These new user stories can be used as the base user stories in future similar projects.

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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...

show abstract

Graph-based biomedical text summarization: An itemset mining and sentence clustering approach

Cited by 61 publications

References 35 publications

Unstructured Text Documents Summarization With Multi-Stage Clustering

Unstructured Text Documents Summarization With Multi-Stage Clustering

Automatic summarising of user stories in order to be reused in future similar projects

Intelligent deep learning‐based hierarchical clustering for unstructured text data

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