{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:22:12Z","timestamp":1757542932601},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"11","license":[{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T00:00:00Z","timestamp":1668038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2023,5]]},"DOI":"10.1007\/s11042-022-14171-6","type":"journal-article","created":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T12:03:31Z","timestamp":1668081811000},"page":"17175-17194","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Modeling of automated glowworm swarm optimization based deep learning model for legal text summarization"],"prefix":"10.1007","volume":"82","author":[{"given":"V.","family":"Vaissnave","sequence":"first","affiliation":[]},{"given":"P.","family":"Deepalakshmi","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,11,10]]},"reference":[{"issue":"13","key":"14171_CR1","doi-asserted-by":"publisher","first-page":"19567","DOI":"10.1007\/s11042-021-10613-9","volume":"80","author":"N Alami","year":"2021","unstructured":"Alami N, Mallahi ME, Amakdouf H, Qjidaa H (2021) Hybrid method for text summarization based on statistical and semantic treatment. Multimed Tools Appl 80(13):19567\u201319600","journal-title":"Multimed Tools Appl"},{"issue":"4","key":"14171_CR2","doi-asserted-by":"publisher","first-page":"651","DOI":"10.1007\/s12559-018-9547-z","volume":"10","author":"QA Al-Radaideh","year":"2018","unstructured":"Al-Radaideh QA, Bataineh DQ (2018) A hybrid approach for Arabic text summarization using domain knowledge and genetic algorithms. Cogn Comput 10(4):651\u2013669","journal-title":"Cogn Comput"},{"issue":"2","key":"14171_CR3","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1007\/s10462-015-9442-x","volume":"45","author":"AB Al-Saleh","year":"2016","unstructured":"Al-Saleh AB, Menai MEB (2016) Automatic Arabic text summarization: a survey. Artif Intell Rev 45(2):203\u2013234","journal-title":"Artif Intell Rev"},{"key":"14171_CR4","unstructured":"Anand D, Wagh R (2019) Effective deep learning approaches for summarization of legal texts. J King Saud Univ-Comput Inf Sci"},{"key":"14171_CR5","doi-asserted-by":"crossref","unstructured":"Bhattacharya P, Hiware K, Rajgaria S, Pochhi N, Ghosh K, Ghosh S (2019) A comparative study of summarization algorithms applied to legal case judgments. In: European conference on information retrieval. Springer, Cham, pp 413\u2013428","DOI":"10.1007\/978-3-030-15712-8_27"},{"key":"14171_CR6","doi-asserted-by":"crossref","unstructured":"Bhattacharya P, Poddar S, Rudra K, Ghosh K, Ghosh S (2021) Incorporating domain knowledge for extractive summarization of legal case documents. In: Proceedings of the eighteenth international conference on artificial intelligence and law, pp 22\u201331","DOI":"10.1145\/3462757.3466092"},{"key":"14171_CR7","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y Learning phrase representations using rnnencoderdecoder for statistical machine translation. arXiv preprint arXiv:1406.1078"},{"key":"14171_CR8","doi-asserted-by":"crossref","unstructured":"Feijo DDV, Moreira VP (2021) Improving abstractive summarization of legal rulings through textual entailment. Artificial intelligence and law, pp 1\u201323","DOI":"10.1007\/s10506-021-09305-4"},{"key":"14171_CR9","doi-asserted-by":"crossref","unstructured":"Hou W, Jin Y, Zhu C, Li G (2016) A novel maximum power point tracking algorithm based on glowworm swarm optimization for photovoltaic systems. Int J Photoenergy\u00a02016","DOI":"10.1155\/2016\/4910862"},{"issue":"6","key":"14171_CR10","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11704-021-0561-z","volume":"16","author":"Y Huang","year":"2022","unstructured":"Huang Y, Yu Z, Xiang Y, Yu Z, Guo J (2022) Exploiting comments information to improve legal public opinion news abstractive summarization. Front Comput Sci 16(6):1\u201310","journal-title":"Front Comput Sci"},{"issue":"11","key":"14171_CR11","doi-asserted-by":"publisher","first-page":"635","DOI":"10.3390\/ijgi9110635","volume":"9","author":"P Li","year":"2020","unstructured":"Li P, Luo A, Liu J, Wang Y, Zhu J, Deng Y, Zhang J (2020) Bidirectional gated recurrent unit neural network for Chinese address element segmentation. ISPRS Int J Geo-Inf 9(11):635","journal-title":"ISPRS Int J Geo-Inf"},{"key":"14171_CR12","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.neucom.2019.09.012","volume":"371","author":"F Liu","year":"2020","unstructured":"Liu F, Zheng J, Zheng L, Chen C (2020) Combining attention-based bidirectional gated recurrent neural network and two-dimensional convolutional neural network for document-level sentiment classification. Neurocomputing 371:39\u201350","journal-title":"Neurocomputing"},{"key":"14171_CR13","doi-asserted-by":"crossref","unstructured":"Merchant K, Pande Y (2018) Nlp based latent semantic analysis for legal text summarization. In:\u00a02018 International Conference on Advances in Computing, Communications and Informatics (ICACCI). IEEE, pp\u00a01803\u20131807","DOI":"10.1109\/ICACCI.2018.8554831"},{"key":"14171_CR14","doi-asserted-by":"publisher","first-page":"105117","DOI":"10.1016\/j.cmpb.2019.105117","volume":"184","author":"M Moradi","year":"2020","unstructured":"Moradi M, Dorffner G, Samwald M (2020) Deep contextualized embeddings for quantifying the informative content in biomedical text summarization. Comput Meth Prog Biomed 184:105117","journal-title":"Comput Meth Prog Biomed"},{"key":"14171_CR15","doi-asserted-by":"crossref","unstructured":"Muthu B, CB S, Kumar PM, Kadry SN, Hsu CH, Sanjuan O, Crespo RG (2020) A framework for extractive text summarization based on deep learning modified neural network classifier. ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)","DOI":"10.1145\/3392048"},{"issue":"10","key":"14171_CR16","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.3390\/sym11101290","volume":"11","author":"M Rahman","year":"2019","unstructured":"Rahman M, Siddiqui FH (2019) An optimized abstractive text summarization model using peephole convolutional LSTM. Symmetry 11(10):1290","journal-title":"Symmetry"},{"issue":"3","key":"14171_CR17","doi-asserted-by":"publisher","first-page":"3275","DOI":"10.1007\/s11042-020-09549-3","volume":"80","author":"R Rani","year":"2021","unstructured":"Rani R, Lobiyal DK (2021) An extractive text summarization approach using tagged-LDA based topic modeling. Multimed Tools Appl 80(3):3275\u20133305","journal-title":"Multimed Tools Appl"},{"issue":"2","key":"14171_CR18","doi-asserted-by":"publisher","first-page":"1113","DOI":"10.1007\/s00500-020-05207-w","volume":"25","author":"RK Roul","year":"2021","unstructured":"Roul RK (2021) Topic modeling combined with classification technique for extractive multi-document text summarization. Soft Comput 25(2):1113\u20131127","journal-title":"Soft Comput"},{"key":"14171_CR19","doi-asserted-by":"crossref","unstructured":"Rush AM, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization. In: Proceedings of the conference on empirical methods in natural language processing, Lisbon, Portugal, 2015","DOI":"10.18653\/v1\/D15-1044"},{"key":"14171_CR20","doi-asserted-by":"crossref","unstructured":"Sheik R, Nirmala SJ (2021) Deep learning techniques for legal text summarization. In:\u00a02021 IEEE 8th Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON). IEEE, pp 1\u20135","DOI":"10.1109\/UPCON52273.2021.9667640"},{"issue":"1","key":"14171_CR21","doi-asserted-by":"publisher","first-page":"857","DOI":"10.1007\/s11042-018-5749-3","volume":"78","author":"S Song","year":"2019","unstructured":"Song S, Huang H, Ruan T (2019) Abstractive text summarization using LSTM-CNN based deep learning. Multimed Tools Appl 78(1):857\u2013875","journal-title":"Multimed Tools Appl"},{"key":"14171_CR22","doi-asserted-by":"crossref","unstructured":"Soni V, Kumar L, Singh AK, Kumar M (2020) Text summarization: an extractive approach. In: Soft computing: theories and applications. Springer, Singapore, pp 629\u2013637","DOI":"10.1007\/978-981-15-4032-5_57"},{"issue":"7","key":"14171_CR23","doi-asserted-by":"publisher","first-page":"11273","DOI":"10.1007\/s11042-020-10176-1","volume":"80","author":"AK Srivastava","year":"2021","unstructured":"Srivastava AK, Pandey D, Agarwal A (2021) Extractive multi-document text summarization using dolphin swarm optimization approach. Multimed Tools Appl 80(7):11273\u201311290","journal-title":"Multimed Tools Appl"},{"key":"14171_CR24","doi-asserted-by":"crossref","unstructured":"Suleiman D, Awajan A (2020) Deep learning based abstractive text summarization: approaches, datasets, evaluation measures, and challenges. Math Prob Eng\u00a02020","DOI":"10.1155\/2020\/9365340"},{"key":"14171_CR25","doi-asserted-by":"crossref","unstructured":"Sun Y, Yang F, Wang X, Dong H (2021) Automatic generation of the draft procuratorial suggestions based on an extractive summarization method: BERTSLCA. Math Prob Eng\u00a02021","DOI":"10.1155\/2021\/3591894"},{"key":"14171_CR26","doi-asserted-by":"publisher","first-page":"25","DOI":"10.1016\/j.procs.2016.05.121","volume":"87","author":"C Sunitha","year":"2016","unstructured":"Sunitha C, Jaya A, Ganesh A (2016) A study on abstractive summarization techniques in Indian languages. Procedia Comput Sci 87:25\u201331","journal-title":"Procedia Comput Sci"},{"key":"14171_CR27","doi-asserted-by":"crossref","unstructured":"Thomas J, Sreeraj A, Sreeraj A, Varghese MM, Kuriakose T (2022) Automatic text summarization using deep learning and reinforcement learning. In: Sentimental analysis and deep learning. Springer, Singapore, pp 769\u2013778","DOI":"10.1007\/978-981-16-5157-1_60"},{"key":"14171_CR28","doi-asserted-by":"crossref","unstructured":"Tiwari A, Dembla D (2019) A novel algorithm for automatic text summarization system using lexical chain. In: Ambient communications and computer systems. Springer, Singapore, pp 103\u2013112","DOI":"10.1007\/978-981-13-5934-7_10"},{"key":"14171_CR29","doi-asserted-by":"crossref","unstructured":"Wagh RS, Anand D (2020) A novel approach of augmenting training data for legal text segmentation by leveraging domain knowledge. In: Intelligent systems, technologies and applications. Springer, Singapore, pp 53\u201363","DOI":"10.1007\/978-981-13-6095-4_4"},{"issue":"8","key":"14171_CR30","doi-asserted-by":"publisher","first-page":"1665","DOI":"10.3390\/app9081665","volume":"9","author":"Y Zhang","year":"2019","unstructured":"Zhang Y, Li D, Wang Y, Fang Y, Xiao W (2019) Abstract text summarization with a convolutional Seq2seq model. Appl Sci 9(8):1665","journal-title":"Appl Sci"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14171-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-022-14171-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-022-14171-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,4,15]],"date-time":"2023-04-15T09:30:42Z","timestamp":1681551042000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-022-14171-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,10]]},"references-count":30,"journal-issue":{"issue":"11","published-print":{"date-parts":[[2023,5]]}},"alternative-id":["14171"],"URL":"https:\/\/doi.org\/10.1007\/s11042-022-14171-6","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"type":"print","value":"1380-7501"},{"type":"electronic","value":"1573-7721"}],"subject":[],"published":{"date-parts":[[2022,11,10]]},"assertion":[{"value":"9 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 February 2022","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 October 2022","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 November 2022","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Human participants and\/or animals"}},{"value":"The authors have expressed no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}