Experimental analysis of multiple criteria for extractive multi-document text summarization

Sánchez‐Gómez, Jesús; Vega‐Rodríguez, Miguel A.; Pérez, Carlos J.

doi:10.1016/j.eswa.2019.112904

Cited by 29 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Every text analysis initiates from the text pre-processing and mostly include the dictionary analysis. This step remains intact in many applications of natural language processing [4], [7], [11]. Assessing the existence of the desired text in unstructured text documents requires a multi-document review.…”

Section: Discussionmentioning

confidence: 99%

“…All the webpages and informal documents have keywords or page rank keywords to represent the actual text document's theme. TF-IDF and PRK generally help to assess the contents of relevant text documents [7], [16], [26]. However, for in-depth text assessments, multiple text processing techniques require systematically joined to relate text segments [9], [14].…”

Section: Discussionmentioning

confidence: 99%

“…X ! This approach lack in rule-based automatic text analysis Combinational analysis of text documents for multi-document summarization [7] ! X !…”

Section: Concept-based Abstractive and Extractive Text Mining Implementmentioning

confidence: 99%

“…The efficiency of text-documents grouping decreases the size of the large corpus. However, grouping text documents by splitting multiple text-documents into related subsets is a problematic text processing task [1], [3], [7]. Furthermore, the efficiency of information retrieval approaches decreases in the absence of such grouping of corpus [2], [3].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

“…X ! This approach lack in rule-based automatic text analysis Combinational analysis of text documents for multi-document summarization [7] ! X !…”

Section: Concept-based Abstractive and Extractive Text Mining Implementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Unstructured Text Documents Summarization With Multi-Stage Clustering

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The approach is evaluated on DUC2004 and BBC news datasets using evaluation metrics such as precision, recall and F1 score. Sanchez-Gomez et al [51] performs a comparative study of disparate criterions applicable for generic extractive summarization for multi-documents. In this, authors execute experiments on DUC datasets with all possible combinations of various criterions, namely Content Coverage, Redundancy Reduction, Relevance and Coherence.…”

Section: A Extractive Summarizationmentioning

confidence: 99%

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Jindal

Kaur

2020

IEEE Access

View full text Add to dashboard Cite

Text Summarization is a process which efficiently retrieves the relevant information from documents. The objective of the proposed, unsupervised approach is to summarize bug reports (software artefacts) with complete content and diversified information. The proposed approach utilizes Rapid Automatic Keyword Extraction and term frequency-inverse document frequency method to extract meaningful keywords and key-phrases with a relevant score. For sentence extraction, fuzzy C-means clustering is used to extracts sentences having high degree of membership from each cluster above a set threshold value. A ruleengine is used for sentence selection. The rules are generated with the domain knowledge and based on the extracted information by the keywords and sentences selected by the clustering method. Cohesive and coherent summary is generated by the proposed method on apache bug reports. For redundancy removal and to re-rank generated summary, hierarchical clustering is presented to enrich the extracted summary. The proposed approach is evaluated on newly constructed Apache project Bug Report Corpus (APBRC) and existing Bug Report Corpus (BRC). The results are compared on the basis of performance metrics such as precision, recall, pyramid precision and F-score. The experimental results depict that our proposed approach attains significant improvement over other baseline approaches such as BRC and LRCA. It also attains significant improvement over existing state-of-art unsupervised approaches such as Hurried, centroid and others. It extracts significant keyword phrases and sentences from each cluster to achieve full coverage and coherent summary. The results evaluated on APBRC corpus attains an average value of 78.22%, 82.18%, 80.10% and 81.66% for precision, recall, f-score and pyramid precision respectively. INDEX TERMS Text summarization, rapid automatic keyword extraction, fuzzy c-means, hierarchical clustering, bug reports, rule engine.

show abstract

Intelligent deep learning‐based hierarchical clustering for unstructured text data

Jyothi

Lingamgunta

Eluri

2022

Concurrency and Computation

View full text Add to dashboard Cite

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

Experimental analysis of multiple criteria for extractive multi-document text summarization

Cited by 29 publications

References 21 publications

Unstructured Text Documents Summarization With Multi-Stage Clustering

Unstructured Text Documents Summarization With Multi-Stage Clustering

Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports

Intelligent deep learning‐based hierarchical clustering for unstructured text data

Contact Info

Product

Resources

About