2022
DOI: 10.1016/j.knosys.2022.108636
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A topic modeled unsupervised approach to single document extractive text summarization

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Cited by 49 publications
(16 citation statements)
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References 38 publications
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“…The method’s primary contribution is a general mechanism that reduces the input document’s dimension to the topic vector, enabling the comparison of sentences with the vector and achieving impressive results in terms of rouge parameters. In [ 32 ], the authors introduced an unsupervised method for extractive summarization which combines K-Medoids clustering and Latent Dirichlet Allocation (LDA) topic modeling to minimize topic bias. The findings of this study demonstrate that this approach, with a stronger emphasis on subtopics, outperforms conventional topic modeling and deep learning approaches in unsupervised extractive summarization.…”
Section: Related Workmentioning
confidence: 99%
“…The method’s primary contribution is a general mechanism that reduces the input document’s dimension to the topic vector, enabling the comparison of sentences with the vector and achieving impressive results in terms of rouge parameters. In [ 32 ], the authors introduced an unsupervised method for extractive summarization which combines K-Medoids clustering and Latent Dirichlet Allocation (LDA) topic modeling to minimize topic bias. The findings of this study demonstrate that this approach, with a stronger emphasis on subtopics, outperforms conventional topic modeling and deep learning approaches in unsupervised extractive summarization.…”
Section: Related Workmentioning
confidence: 99%
“…An ontology-based dynamic information extraction framework identifies a wide range of document resources published in the scientific community and extracts the whole structural information [35][36][37][38][39][40][41]. e accuracy and scope of information extraction can be improved using an entity-relationship-based framework [42][43][44][45][46][47]. Few research works employed the term-frequency methodology for ranking the webpages [48][49][50][51][52][53][54].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Automatic keyphrase extraction is used in many domains dealing with textual data, such as text classification [5], document clustering [6], document summarization [7], and search engines [8]. Although some studies have attempted to limit these domains like [9], which limited their use to five domains, due to importance of the information provided by the keyphrases, the AKE can also be exploited in many other domains such as recommender systems [10], web mining [11], bibliometric analysis [12], and sentiment analysis [13].…”
Section: Applicationsmentioning
confidence: 99%