{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T18:46:10Z","timestamp":1775069170634,"version":"3.50.1"},"reference-count":20,"publisher":"Association for Computing Machinery (ACM)","issue":"3","license":[{"start":{"date-parts":[[2021,5,31]],"date-time":"2021-05-31T00:00:00Z","timestamp":1622419200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["61872084"],"award-info":[{"award-number":["61872084"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Joint Laboratory for Intelligent Micro-Nano Optoelectronic Technology","award":["2020B1212030010"],"award-info":[{"award-number":["2020B1212030010"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Asian Low-Resour. Lang. Inf. Process."],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>There is an exponential growth of text data over the internet, and it is expected to gain significant growth and attention in the coming years. Extracting meaningful insights from text data is crucially important as it offers value-added solutions to business organizations and end-users. Automatic text summarization (ATS) automates text summarization by reducing the initial size of the text without the loss of key information elements. In this article, we propose a novel text summarization algorithm for documents using Deep Learning Modifier Neural Network (DLMNN) classifier. It generates an informative summary of the documents based on the entropy values. The proposed DLMNN framework comprises six phases. In the initial phase, the input document is pre-processed. Subsequently, the features are extracted using pre-processed data. Next, the most appropriate features are selected using the improved fruit fly optimization algorithm (IFFOA). The entropy value for every chosen feature is computed. These values are then classified into two classes, (a) highest entropy values and (b) lowest entropy values. Finally, the class that holds the highest entropy values is chosen, representing the informative sentences that form the last summary. The results observed from the experiment indicate that the DLMNN classifier gives 81.56, 91.21, and 83.53 of sensitivity, accuracy, specificity, precision, and f-measure. Whereas the existing schemes such as ANN relatively provide lesser value in contrast to DLMNN.<\/jats:p>","DOI":"10.1145\/3392048","type":"journal-article","created":{"date-parts":[[2020,7,7]],"date-time":"2020-07-07T12:39:07Z","timestamp":1594125547000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":43,"title":["A Framework for Extractive Text Summarization Based on Deep Learning Modified Neural Network Classifier"],"prefix":"10.1145","volume":"20","author":[{"given":"Balaanand","family":"Muthu","sequence":"first","affiliation":[{"name":"V.R.S. College of Engineering &amp; Technology, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sivaparthipan","family":"Cb","sequence":"additional","affiliation":[{"name":"SNS College of Technology, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Priyan Malarvizhi","family":"Kumar","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Kyung Hee University, South Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seifedine Nimer","family":"Kadry","sequence":"additional","affiliation":[{"name":"Beirut Arab University, Lebanon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ching-Hsien","family":"Hsu","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Information Engineering, Asia University, Taiwan;\u00a0Department of Medical Research, China Medical University Hospital, China Medical University, Taiwan; School of Mathematics and Big Data, Foshan University, Foshan 528000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oscar","family":"Sanjuan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UNIR Universidad Internacional de La Rioja, La Rioja, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruben Gonzalez","family":"Crespo","sequence":"additional","affiliation":[{"name":"Department of Computer Science, UNIR Universidad Internacional de La Rioja, La Rioja, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,7,14]]},"reference":[{"key":"e_1_2_1_1_1","article-title":"An overview of extractive-based automatic text summarization systems","volume":"8","author":"Mubarak D. Muhammad Noorul","year":"2016","unstructured":"D. Muhammad Noorul Mubarak . 2016 . An overview of extractive-based automatic text summarization systems . Int. J. Comput. Sci. Info. Technol. 8 , 5 (2016). D. Muhammad Noorul Mubarak. 2016. An overview of extractive-based automatic text summarization systems. Int. J. Comput. Sci. Info. Technol. 8, 5 (2016).","journal-title":"Int. J. Comput. Sci. Info. Technol."},{"key":"e_1_2_1_2_1","first-page":"1","article-title":"An overview of text summarization","volume":"171","author":"Laxmi Rananavare B.","year":"2017","unstructured":"B. Laxmi Rananavare and P. Venkata Subba Reddy . 2017 . An overview of text summarization . Int. J. Comput. Appl. 171 , 10 (2017), 1 \u2013 17 . B. Laxmi Rananavare and P. Venkata Subba Reddy. 2017. An overview of text summarization. Int. J. Comput. Appl. 171, 10 (2017), 1\u201317.","journal-title":"Int. J. Comput. Appl."},{"key":"e_1_2_1_3_1","volume-title":"Automatic text summarization: What has been done and what has to be done. Comput. Lang","author":"Aries Abdelkrime","year":"2019","unstructured":"Abdelkrime Aries and Walid Khaled Hidouci . 2019. Automatic text summarization: What has been done and what has to be done. Comput. Lang . 2019 . Abdelkrime Aries and Walid Khaled Hidouci. 2019. Automatic text summarization: What has been done and what has to be done. Comput. Lang. 2019."},{"key":"e_1_2_1_4_1","volume-title":"Wasel Ghanem, Mahdi Washaha, and Eman Maali.","author":"Qaroush Aziz","year":"2019","unstructured":"Aziz Qaroush , Ibrahim Abu Farha , Wasel Ghanem, Mahdi Washaha, and Eman Maali. 2019 . An efficient single document arabic text summarization using a combination of statistical and semantic features. J. King Saud Univ.-Comput . Info. Sci. 2019. Aziz Qaroush, Ibrahim Abu Farha, Wasel Ghanem, Mahdi Washaha, and Eman Maali. 2019. An efficient single document arabic text summarization using a combination of statistical and semantic features. J. King Saud Univ.-Comput. Info. Sci. 2019."},{"key":"e_1_2_1_5_1","unstructured":"Wan-Ting Hsu Chieh-Kai Lin Ming-Ying Lee Kerui Min Jing Tang and Min Sun. 2018. A unified model for extractive and abstractive summarization using inconsistency loss. Comput. Language. Retrieved from https:\/\/arxiv.org\/abs\/1805.06266.  Wan-Ting Hsu Chieh-Kai Lin Ming-Ying Lee Kerui Min Jing Tang and Min Sun. 2018. A unified model for extractive and abstractive summarization using inconsistency loss. Comput. Language. Retrieved from https:\/\/arxiv.org\/abs\/1805.06266."},{"key":"e_1_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1017\/S0266078418000068"},{"key":"e_1_2_1_7_1","first-page":"258","article-title":"A survey of text summarization extractive techniques","volume":"2","author":"Gupta Vishal","year":"2010","unstructured":"Vishal Gupta and Gurpreet Singh Lehal . 2010 . A survey of text summarization extractive techniques . J. Emerg. Technol. Web Intell. 2 , 3 (2010), 258 \u2013 268 . Vishal Gupta and Gurpreet Singh Lehal. 2010. A survey of text summarization extractive techniques. J. Emerg. Technol. Web Intell. 2, 3 (2010), 258\u2013268.","journal-title":"J. Emerg. Technol. Web Intell."},{"key":"e_1_2_1_8_1","volume-title":"Proceedings of the International Conference on Circuit, Power and Computing Technologies (ICCPCT\u201916)","author":"Moratanch N.","unstructured":"N. Moratanch and S. Chitrakala . 2016. A survey on abstractive text summarization . In Proceedings of the International Conference on Circuit, Power and Computing Technologies (ICCPCT\u201916) , IEEE, 1\u20137. N. Moratanch and S. Chitrakala. 2016. A survey on abstractive text summarization. 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