{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,15]],"date-time":"2024-09-15T07:09:43Z","timestamp":1726384183557},"reference-count":31,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,12,22]],"date-time":"2021-12-22T00:00:00Z","timestamp":1640131200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,3,15]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper proposes a multi-document summarization model using an optimization algorithm named CAVIAR Sun Flower Optimization (CAV-SFO). In this method, two classifiers, namely: Generative Adversarial Network (GAN) classifier and Deep Recurrent Neural Network (Deep RNN), are utilized to generate a score for summarizing multi-documents. Initially, the simHash method is applied for removing the duplicate\/real duplicate contents from sentences. Then, the result is given to the proposed CAV-SFO based GAN classifier to determine the score for individual sentences. The CAV-SFO is newly designed by incorporating CAVIAR with Sun Flower Optimization Algorithm (SFO). On the other hand, the pre-processing step is done for duplicate-removed sentences from input multi-document based on stop word removal and stemming. Afterward, text-based features are extracted from pre-processed documents, and then CAV-SFO based Deep RNN is introduced for generating a score; thereby, the internal model parameters are optimally tuned. Finally, the score generated by CAV-SFO based GAN and CAV-SFO based Deep RNN is hybridized, and the final score is obtained using a multi-document compression ratio. The proposed TaylorALO-based GAN showed improved results with maximal precision of 0.989, maximal recall of 0.986, maximal F-Measure of 0.823, maximal Rouge-Precision of 0.930, and maximal Rouge-recall of 0.870.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab193","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T12:08:37Z","timestamp":1640088517000},"page":"727-742","source":"Crossref","is-referenced-by-count":1,"title":["Caviar-Sunflower Optimization Algorithm-Based Deep Learning Classifier for Multi-Document Summarization"],"prefix":"10.1093","volume":"66","author":[{"given":"Sheela","family":"J","sequence":"first","affiliation":[{"name":"Research Scholar , , Tiruchirappalli, Tamil Nadu 620015"},{"name":"National Institute of Technology , , Tiruchirappalli, Tamil Nadu 620015"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Janet","family":"B","sequence":"additional","affiliation":[{"name":"Assistant Professor , , Tiruchirappalli, Tamil Nadu 620015"},{"name":"National Institute of Technology , , Tiruchirappalli, Tamil Nadu 620015"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2021,12,22]]},"reference":[{"key":"2023031708554238800_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.knosys.2017.11.029","article-title":"Extractive multi-document text summarization using a multi-objective artificial bee colony optimization approach","volume":"159","author":"Sanchez-Gomez","year":"2018","journal-title":"Knowledge-Based Systems"},{"key":"2023031708554238800_","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.eswa.2019.05.045","article-title":"Fuzzy logic based multi document summarization with improved sentence scoring and redundancy removal technique","volume":"134","author":"Patel","year":"2019","journal-title":"Expert Systems with Applications"},{"key":"2023031708554238800_","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.swevo.2011.06.006","article-title":"Sentence selection for generic document summarization using an adaptive differential evolution algorithm","volume":"1","author":"Alguliev","year":"2011","journal-title":"Swarm and Evolutionary Computation"},{"key":"2023031708554238800_","doi-asserted-by":"crossref","first-page":"120375","DOI":"10.1109\/ACCESS.2019.2936832","article-title":"An approach for combining multiple weighting schemes and ranking methods in graph-based multi-document summarization","volume":"7","author":"Alzuhair","year":"2019","journal-title":"IEEE Access"},{"key":"2023031708554238800_","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/j.artmed.2017.11.004","article-title":"Different approaches for identifying important concepts in probabilistic biomedical text summarization","volume":"84","author":"Moradi","year":"2018","journal-title":"Artif. 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