{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T00:37:38Z","timestamp":1743122258571,"version":"3.40.3"},"publisher-location":"Cham","reference-count":18,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466632"},{"type":"electronic","value":"9783031466649"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46664-9_21","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"307-319","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["SUMOPE: Enhanced Hierarchical Summarization Model for\u00a0Long Texts"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1139-4781","authenticated-orcid":false,"given":"Chao","family":"Chang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0000-1205-5154","authenticated-orcid":false,"given":"Junming","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3171-4618","authenticated-orcid":false,"given":"Xiangwei","family":"Zeng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9812-0742","authenticated-orcid":false,"given":"Yong","family":"Tang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","unstructured":"Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159\u2013165 (1958). https:\/\/doi.org\/10.1147\/rd.22.0159","DOI":"10.1147\/rd.22.0159"},{"issue":"5","key":"21_CR2","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1108\/00220410410560573","volume":"60","author":"KS Jones","year":"2004","unstructured":"Jones, K.S.: A statistical interpretation of term specificity and its application in retrieval. J. Documentation 60(5), 493\u2013502 (2004)","journal-title":"J. Documentation"},{"key":"21_CR3","unstructured":"Cao, Z., Wei, F., Dong, L., Li, S., Zhou, M.: Ranking with recursive neural networks and its application to multi-document summarization. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 25\u201330 January 2015, Austin, Texas, USA, pp. 2153\u20132159. AAAI Press (2015)"},{"key":"21_CR4","unstructured":"Nallapati, R., Zhai, F., Zhou, B.: SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents. In: Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 4\u20139 February 2017, San Francisco, California, USA, pp. 3075\u20133081. AAAI Press (2017)"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lapata, M.: Text summarization with pretrained encoders. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3\u20137 November 2019, pp. 3728\u20133738. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/D19-1387"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Huang, Y.J., Kurohashi, S.: Extractive summarization considering discourse and coreference relations based on heterogeneous graph. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, EACL 2021, Online, 19\u201323 April 2021, pp. 3046\u20133052. Association for Computational Linguistics (2021)","DOI":"10.18653\/v1\/2021.eacl-main.265"},{"key":"21_CR7","doi-asserted-by":"publisher","unstructured":"Ma, C., Zhang, W.E., Guo, M., Wang, H., Sheng, Q.Z.: Multi-document summarization via deep learning techniques: a survey. ACM Comput. Surv. 55(5), 102:1\u2013102:37 (2023). https:\/\/doi.org\/10.1145\/3529754","DOI":"10.1145\/3529754"},{"key":"21_CR8","unstructured":"Dong, L., et al.: Unified language model pre-training for natural language understanding and generation. In: Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Vancouver, BC, Canada, 8\u201314 December 2019, pp. 13042\u201313054 (2019)"},{"key":"21_CR9","unstructured":"Zhang, J., Zhao, Y., Saleh, M., Liu, P.J.: PEGASUS: pre-training with extracted gap-sentences for abstractive summarization. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13\u201318 July 2020, Virtual Event. Proceedings of Machine Learning Research, vol. 119, pp. 11328\u201311339. PMLR (2020)"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, P., Radev, D.R., Neubig, G.: BRIO: bringing order to abstractive summarization. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, 22\u201327 May 2022, pp. 2890\u20132903. Association for Computational Linguistics (2022)","DOI":"10.18653\/v1\/2022.acl-long.207"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Chen, Y., Bansal, M.: Fast abstractive summarization with reinforce-selected sentence rewriting. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, 15\u201320 July 2018, Volume 1: Long Papers, pp. 675\u2013686. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-1063"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: EASE: extractive-abstractive summarization end-to-end using the information bottleneck principle. In: Proceedings of the Third Workshop on New Frontiers in Summarization, pp. 85\u201395 (2021)","DOI":"10.18653\/v1\/2021.newsum-1.10"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Xiong, Y., Racharak, T., Nguyen, M.L.: Extractive elementary discourse units for improving abstractive summarization. In: SIGIR 2022: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 11\u201315 July 2022, pp. 2675\u20132679. ACM (2022)","DOI":"10.1145\/3477495.3531916"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Liu, Y., Titov, I., Lapata, M.: Single document summarization as tree induction. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2019, Minneapolis, MN, USA, 2\u20137 June 2019, Volume 1 (Long and Short Papers), pp. 1745\u20131755. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/N19-1173"},{"key":"21_CR15","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"942","DOI":"10.1007\/978-3-319-73618-1_84","volume-title":"Natural Language Processing and Chinese Computing","author":"L Hua","year":"2018","unstructured":"Hua, L., Wan, X., Li, L.: Overview of the NLPCC 2017 shared task: single document summarization. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Yu. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 942\u2013947. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73618-1_84"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Wei, F., Yang, J., Mao, Q., Qin, H., Dabrowski, A.: An empirical comparison of distilBERT, longformer and logistic regression for predictive coding. In: IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17\u201320 December 2022, pp. 3336\u20133340. IEEE (2022)","DOI":"10.1109\/BigData55660.2022.10020486"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, Canada, 30 July\u20134 August, Volume 1: Long Papers, pp. 1073\u20131083. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/P17-1099"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46664-9_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T11:08:46Z","timestamp":1730459326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46664-9_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466632","9783031466649"],"references-count":18,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46664-9_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"216","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"43% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.97","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.77","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}