Extractive single document summarization using multi-objective optimization: Exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm

Saini, Naveen; Saha, Sriparna; Jangra, Anubhav; Bhattacharyya, Pushpak

doi:10.1016/j.knosys.2018.10.021

Cited by 62 publications

(31 citation statements)

References 36 publications

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“…A lot of research has been done in the area of extractive [10,21] and abstractive [3,4,19,23] summarization. Various techniques like graph-based methods [6,15,16], artificial neural networks [22] and deep learning based approaches [18,20,29] have been developed for text summarization. Integer linear programming (ILP) has also shown promising results in extractive document summarization [1,9].…”

Section: Related Workmentioning

confidence: 99%

Text-Image-Video Summary Generation Using Joint Integer Linear Programming

Jangra

Jatowt

Hasanuzzaman

et al. 2020

Lecture Notes in Computer Science

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Automatically generating a summary for asynchronous data can help users to keep up with the rapid growth of multi-modal information on the Internet. However, the current multi-modal systems usually generate summaries composed of text and images. In this paper, we propose a novel research problem of text-image-video summary generation (TIVS). We first develop a multi-modal dataset containing text documents, images and videos. We then propose a novel joint integer linear programming multi-modal summarization (JILP-MMS) framework. We report the performance of our model on the developed dataset.

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Section: Related Workmentioning

confidence: 99%

Text-Image-Video Summary Generation Using Joint Integer Linear Programming

Jangra

Jatowt

Hasanuzzaman

et al. 2020

Lecture Notes in Computer Science

Self Cite

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“…KMC has an iterative stabilization process for cluster centroids [29], [39], [43]. The main characteristic of a cluster is its centroid.…”

Section: F K-means Clusteringmentioning

confidence: 99%

“…The element f (i, j) has the edge (i, j) attributes [20], [39]. The edges without attributes have a graph representation as a matrix to save memory, as shown in Figure 1.…”

Section: H Adjacency Matrixmentioning

confidence: 99%

“…Recently, the frequency-based weighting techniques are the preliminary researches in sentence extraction studies [9], [14], [31]. Similarly, the semantic assessment by latent analysis [19], [27], [39], Markov-models, and graph-oriented supervised and unsupervised techniques are under investigation [4], [16], [27]. In short, the extractive-summarization is a key area of research as per the current literature analysis [2], [18], [29], [37].…”

Section: Introductionmentioning

confidence: 99%

“…In literature, researchers mostly apply either syntactic and semantic approaches for deciding sentence scores. We implement the predetermined features during the syntactic approach, which are usually hand-crafted for those sentences that we consider for scoring the sentences [8], [13], [22], [39]. Semantic approaches involve the word's meaning and various types of text phrases to create a semantic relation [9], [17], [28], [41].…”

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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Multi-objective Grey Wolf Optimizer

Mirjalili

Dong

2019

Multi-Objective Optimization Using Artificial Intelligence Techniques

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Extractive single document summarization using multi-objective optimization: Exploring self-organized differential evolution, grey wolf optimizer and water cycle algorithm

Cited by 62 publications

References 36 publications

Text-Image-Video Summary Generation Using Joint Integer Linear Programming

Text-Image-Video Summary Generation Using Joint Integer Linear Programming

Unstructured Text Documents Summarization With Multi-Stage Clustering

Multi-objective Grey Wolf Optimizer

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