{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T18:13:46Z","timestamp":1742926426286,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030863821"},{"type":"electronic","value":"9783030863838"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-86383-8_25","type":"book-chapter","created":{"date-parts":[[2021,9,10]],"date-time":"2021-09-10T08:02:49Z","timestamp":1631260969000},"page":"309-320","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Scoring Model Assisted by Frequency for Multi-Document Summarization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5122-6013","authenticated-orcid":false,"given":"Yue","family":"Yu","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7043-781X","authenticated-orcid":false,"given":"Mutong","family":"Wu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9501-0321","authenticated-orcid":false,"given":"Weifeng","family":"Su","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7629-4648","authenticated-orcid":false,"given":"Yiu-ming","family":"Cheung","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,9,7]]},"reference":[{"key":"25_CR1","doi-asserted-by":"publisher","unstructured":"An, N.N., Thanh, N.Q., Liu, Y.: Deep CNNs with self-attention for speaker identification. IEEE Access 7, 85327\u201385337 (2019). https:\/\/doi.org\/10.1109\/ACCESS.2019.2917470, https:\/\/ieeexplore.ieee.org\/document\/8721628\/","DOI":"10.1109\/ACCESS.2019.2917470"},{"key":"25_CR2","doi-asserted-by":"publisher","unstructured":"Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR 1998, New York, New York, USA, pp. 335\u2013336. ACM Press (1998). https:\/\/doi.org\/10.1145\/290941.291025. http:\/\/portal.acm.org\/citation.cfm?doid=290941.291025","DOI":"10.1145\/290941.291025"},{"key":"25_CR3","doi-asserted-by":"publisher","unstructured":"Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. In: 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016 - Long Papers (2016). https:\/\/doi.org\/10.18653\/v1\/p16-1046","DOI":"10.18653\/v1\/p16-1046"},{"key":"25_CR4","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: NAACL HLT 2019\u20132019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Proceedings of the Conference (2019)"},{"key":"25_CR5","doi-asserted-by":"publisher","unstructured":"Edmundson, H.P.: New methods in automatic extracting. J. ACM 16(2), 264\u2013285 (1969). https:\/\/doi.org\/10.1145\/321510.321519. https:\/\/dl.acm.org\/doi\/10.1145\/321510.321519","DOI":"10.1145\/321510.321519"},{"key":"25_CR6","doi-asserted-by":"publisher","unstructured":"Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457\u2013479 (2004). https:\/\/doi.org\/10.1613\/jair.1523. http:\/\/arxiv.org\/abs\/1109.2128, https:\/\/jair.org\/index.php\/jair\/article\/view\/10396","DOI":"10.1613\/jair.1523"},{"key":"25_CR7","doi-asserted-by":"publisher","unstructured":"Fabbri, A.R., Li, I., She, T., Li, S., Radev, D.R.: Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model, pp. 1074\u20131084 (2019). https:\/\/doi.org\/10.18653\/v1\/p19-1102. http:\/\/arxiv.org\/abs\/1906.01749","DOI":"10.18653\/v1\/p19-1102"},{"key":"25_CR8","doi-asserted-by":"publisher","unstructured":"Gao, Y., Zhao, W., Eger, S.: SUPERT: Towards New Frontiers in Unsupervised Evaluation Metrics for Multi-Document Summarization (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.124","DOI":"10.18653\/v1\/2020.acl-main.124"},{"key":"25_CR9","doi-asserted-by":"publisher","unstructured":"Gehrmann, S., Deng, Y., Rush, A.: Bottom-Up Abstractive Summarization (2019). https:\/\/doi.org\/10.18653\/v1\/d18-1443","DOI":"10.18653\/v1\/d18-1443"},{"key":"25_CR10","doi-asserted-by":"publisher","unstructured":"Gehrmann, S., Deng, Y., Rush, A.M.: Bottom-up abstractive summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, August 2018. https:\/\/doi.org\/10.18653\/v1\/d18-1443. http:\/\/arxiv.org\/abs\/1808.10792","DOI":"10.18653\/v1\/d18-1443"},{"key":"25_CR11","doi-asserted-by":"publisher","unstructured":"Hariharan, S., Srinivasan, R.: Studies on graph based approaches for singleand multi document summarizations. Int. J. Comput. Theory Eng. 519\u2013526 (2009). https:\/\/doi.org\/10.7763\/IJCTE.2009.V1.84. http:\/\/www.ijcte.org\/show-26-177-1.html","DOI":"10.7763\/IJCTE.2009.V1.84"},{"key":"25_CR12","doi-asserted-by":"crossref","unstructured":"Katragadda, R.: Sentence Position revisited: a robust light-weight Update Summarization \u2018baseline\u2019 Algorithm. Computational Linguistics (2009)","DOI":"10.3115\/1572433.1572440"},{"key":"25_CR13","doi-asserted-by":"publisher","unstructured":"Kitaev, N., Cao, S., Klein, D.: Multilingual constituency parsing with self-attention and pre-training. In: ACL 2019\u201357th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (2020). https:\/\/doi.org\/10.18653\/v1\/p19-1340","DOI":"10.18653\/v1\/p19-1340"},{"key":"25_CR14","doi-asserted-by":"publisher","unstructured":"Kitaev, N., Klein, D.: Constituency parsing with a self-attentive encoder. In: ACL 2018\u201356th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), May 2018. https:\/\/doi.org\/10.18653\/v1\/p18-1249. http:\/\/arxiv.org\/abs\/1805.01052","DOI":"10.18653\/v1\/p18-1249"},{"key":"25_CR15","doi-asserted-by":"publisher","unstructured":"Lebanoff, L., Song, K., Liu, F.: Adapting the neural encoder-decoder framework from single to multi-document summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018, August 2018. https:\/\/doi.org\/10.18653\/v1\/d18-1446. http:\/\/arxiv.org\/abs\/1808.06218","DOI":"10.18653\/v1\/d18-1446"},{"key":"25_CR16","doi-asserted-by":"publisher","unstructured":"Li, W., Xiao, X., Liu, J., Wu, H., Wang, H., Du, J.: Leveraging Graph to Improve Abstractive Multi-Document Summarization, pp. 6232\u20136243 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.555","DOI":"10.18653\/v1\/2020.acl-main.555"},{"key":"25_CR17","unstructured":"Lin, C.Y.: Rouge: a package for automatic evaluation of summaries. In: Proceedings of the Workshop on Text Summarization Branches Out (WAS 2004) (2004)"},{"key":"25_CR18","doi-asserted-by":"publisher","unstructured":"Lin, C.Y., Hovy, E.: Identifying topics by position. In: Proceedings of the Fifth Conference on Applied Natural Language Processing, Morristown, NJ, USA, pp. 283\u2013290. Association for Computational Linguistics (1997). https:\/\/doi.org\/10.3115\/974557.974599. http:\/\/portal.acm.org\/citation.cfm?doid=974557.974599","DOI":"10.3115\/974557.974599"},{"key":"25_CR19","unstructured":"Lin, Z., et al.: A structured self-attentive sentence embedding. In: 5th International Conference on Learning Representations, ICLR 2017 - Conference Track Proceedings, March 2017. http:\/\/arxiv.org\/abs\/1703.03130"},{"key":"25_CR20","unstructured":"Liu, P.J., et al.: Generating Wikipedia by summarizing long sequences. In: 6th International Conference on Learning Representations, ICLR 2018 - Conference Track Proceedings (2018)"},{"key":"25_CR21","doi-asserted-by":"publisher","first-page":"159","DOI":"10.1147\/rd.22.0159","volume":"2","author":"HP Lunh","year":"1958","unstructured":"Lunh, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2, 159\u2013165 (1958)","journal-title":"IBM J. Res. Dev."},{"key":"25_CR22","unstructured":"Mihalcea, R., Tarau, P.: TextRank: bringing order into texts. In: Proceedings of EMNLP (2004)"},{"key":"25_CR23","unstructured":"Mnih, V., Heess, N., Graves, A., Kavukcuoglu, K.: Recurrent models of visual attention. In: Advances in Neural Information Processing Systems, June 2014. http:\/\/arxiv.org\/abs\/1406.6247"},{"key":"25_CR24","doi-asserted-by":"publisher","unstructured":"Nallapati, R., Zhou, B., dos Santos, C., Gul\u00e7ehre, \u00c7., Xiang, B.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: CoNLL 2016\u201320th SIGNLL Conference on Computational Natural Language Learning, Proceedings (2016). https:\/\/doi.org\/10.18653\/v1\/k16-1028","DOI":"10.18653\/v1\/k16-1028"},{"key":"25_CR25","doi-asserted-by":"publisher","unstructured":"Narayan, S., Cohen, S.B., Lapata, M.: Don\u2019t give me the details, just the summary! topic-aware convolutional neural networks for extreme summarization. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (2018). https:\/\/doi.org\/10.5281\/zenodo.2399762","DOI":"10.5281\/zenodo.2399762"},{"key":"25_CR26","unstructured":"Nenkova, A.: Automatic text summarization of newswire: lessons learned from the document understanding conference. In: Proceedings of the National Conference on Artificial Intelligence (2005)"},{"key":"25_CR27","unstructured":"Ouyang, Y., Li, W., Lu, Q., Zhang, R.: A study on position information in document summarization. In: COLING 2010\u201323rd International Conference on Computational Linguistics, Proceedings of the Conference (2010)"},{"key":"25_CR28","doi-asserted-by":"publisher","unstructured":"Radev, D.R., Jing, H., Sty\u015b, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Process. Manag. 40(6), 919\u2013938 (2004). https:\/\/doi.org\/10.1016\/j.ipm.2003.10.006. https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0306457303000955","DOI":"10.1016\/j.ipm.2003.10.006"},{"key":"25_CR29","doi-asserted-by":"crossref","unstructured":"Schilder, F., Kondadadi, R.: FastSum: fast and accurate query-based multi-document summarization. In: ACL 2008: HLT - 46th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, Proceedings of the Conference (2008)","DOI":"10.3115\/1557690.1557748"},{"key":"25_CR30","doi-asserted-by":"publisher","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. ACL 2017\u201355th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (Long Papers), vol. 1, pp. 1073\u20131083 (2017). https:\/\/doi.org\/10.18653\/v1\/P17-1099","DOI":"10.18653\/v1\/P17-1099"},{"key":"25_CR31","doi-asserted-by":"publisher","unstructured":"Thakkar, K.S., Dharaskar, R.V., Chandak, M.B.: Graph-based algorithms for text summarization. In: 2010 3rd International Conference on Emerging Trends in Engineering and Technology, pp. 516\u2013519. IEEE (2010). https:\/\/doi.org\/10.1109\/ICETET.2010.104. http:\/\/ieeexplore.ieee.org\/document\/5698380\/","DOI":"10.1109\/ICETET.2010.104"},{"key":"25_CR32","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, vol. 30, June 2017. http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"25_CR33","unstructured":"Yih, W.T., Goodman, J., Vanderwende, L., Suzuki, H.: Multi-document summarization by maximizing informative content-words. In: IJCAI International Joint Conference on Artificial Intelligence (2007)"},{"key":"25_CR34","doi-asserted-by":"publisher","unstructured":"Zheng, H., Lapata, M.: Sentence centrality revisited for unsupervised summarization. In: ACL 2019\u201357th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, vol. 2, pp. 6236\u20136247 (2020). https:\/\/doi.org\/10.18653\/v1\/p19-1628","DOI":"10.18653\/v1\/p19-1628"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2021"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-86383-8_25","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T14:44:34Z","timestamp":1709822674000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-86383-8_25"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030863821","9783030863838"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-86383-8_25","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"7 September 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Bratislava","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Slovakia","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 September 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17 September 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2021\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"496","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":"265","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":"4","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":"53% - 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":"3","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":"2.5","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Conference was held online due to the COVID-19 pandemic.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}