{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T15:43:23Z","timestamp":1781019803314,"version":"3.54.1"},"reference-count":98,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T00:00:00Z","timestamp":1692662400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The recent advancements in big data and natural language processing (NLP) have necessitated proficient text mining (TM) schemes that can interpret and analyze voluminous textual data. Text summarization (TS) acts as an essential pillar within recommendation engines. Despite the prevalent use of abstractive techniques in TS, an anticipated shift towards a graph-based extractive TS (ETS) scheme is becoming apparent. The models, although simpler and less resource-intensive, are key in assessing reviews and feedback on products or services. Nonetheless, current methodologies have not fully resolved concerns surrounding complexity, adaptability, and computational demands. Thus, we propose our scheme, GETS, utilizing a graph-based model to forge connections among words and sentences through statistical procedures. The structure encompasses a post-processing stage that includes graph-based sentence clustering. Employing the Apache Spark framework, the scheme is designed for parallel execution, making it adaptable to real-world applications. For evaluation, we selected 500 documents from the WikiHow and Opinosis datasets, categorized them into five classes, and applied the recall-oriented understudying gisting evaluation (ROUGE) parameters for comparison with measures ROUGE-1, 2, and L. The results include recall scores of 0.3942, 0.0952, and 0.3436 for ROUGE-1, 2, and L, respectively (when using the clustered approach). Through a juxtaposition with existing models such as BERTEXT (with 3-gram, 4-gram) and MATCHSUM, our scheme has demonstrated notable improvements, substantiating its applicability and effectiveness in real-world scenarios.<\/jats:p>","DOI":"10.3390\/info14090472","type":"journal-article","created":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T09:10:56Z","timestamp":1692695456000},"page":"472","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Graph-Based Extractive Text Summarization Sentence Scoring Scheme for Big Data Applications"],"prefix":"10.3390","volume":"14","author":[{"given":"Jai Prakash","family":"Verma","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shir","family":"Bhargav","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Madhuri","family":"Bhavsar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1206-2298","authenticated-orcid":false,"given":"Pronaya","family":"Bhattacharya","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amity School of Engineering and Technology, Amity University, Kolkata 700135, West Bengal, India"},{"name":"Research and Innovation Cell, Amity University, Kolkata 700135, West Bengal, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ali","family":"Bostani","sequence":"additional","affiliation":[{"name":"College of Engineering and Applied Sciences, American University of Kuwait, Salmiya 20002, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5679-3140","authenticated-orcid":false,"given":"Subrata","family":"Chowdhury","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Sreenivasa Institute of Technology and Management Studies, Chittoor 517127, Andra Pradesh, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7796-2898","authenticated-orcid":false,"given":"Julian","family":"Webber","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), 7th Ring Road, Kuwait City 13133, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0945-512X","authenticated-orcid":false,"given":"Abolfazl","family":"Mehbodniya","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Kuwait College of Science and Technology (KCST), 7th Ring Road, Kuwait City 13133, Kuwait"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"63314","DOI":"10.1109\/ACCESS.2022.3183108","article-title":"An Opinion Mining Approach to Handle Perspectivism and Ambiguity: Moving Toward Neutrosophic Logic","volume":"10","author":"Essameldin","year":"2022","journal-title":"IEEE Access"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/TNSE.2022.3207418","article-title":"Online Context-Aware Task Assignment in Mobile Crowdsourcing via Adaptive Discretization","volume":"10","author":"Elahi","year":"2023","journal-title":"IEEE Trans. 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