{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T17:17:05Z","timestamp":1742923025043,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031417733"},{"type":"electronic","value":"9783031417740"}],"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\/978-3-031-41774-0_20","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T03:25:20Z","timestamp":1695266720000},"page":"250-261","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["OTSummarizer an\u00a0Optimal Transport Based Approach for\u00a0Extractive Text Summarization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2504-5267","authenticated-orcid":false,"given":"Imen","family":"Tanfouri","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2007-2645","authenticated-orcid":false,"given":"Fethi","family":"Jarray","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"20_CR1","unstructured":"Abu Nada, A.M., Alajrami, E., Al-Saqqa, A.A., Abu-Naser, S.S.: Arabic text summarization using arabert model using extractive text summarization approach (2020)"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lapata, M.: Text summarization with pretrained encoders. arXiv preprint arXiv:1908.08345 (2019)","DOI":"10.18653\/v1\/D19-1387"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Nallapati, R., et al.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. arXiv preprint arXiv:1602.06023 (2016)","DOI":"10.18653\/v1\/K16-1028"},{"key":"20_CR4","unstructured":"Hermann, K.M., et al.: Teaching machines to read and comprehend. Adv. Neural Inf. Process. Syst. 28 (2015)"},{"key":"20_CR5","doi-asserted-by":"crossref","unstructured":"Xu, J., Durrett, G.: Neural extractive text summarization with syntactic compression. arXiv preprint arXiv:1902.00863 (2019)","DOI":"10.18653\/v1\/D19-1324"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Zhong, M., et al.: Extractive summarization as text matching. arXiv preprint arXiv:2004.08795 (2020)","DOI":"10.18653\/v1\/2020.acl-main.552"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Bouscarrat, L., Antoine, B., Thomas, P., C\u00e9cile, P.: STRASS: a light and effective method for extractive summarization based on sentence embeddings. arXiv preprint arXiv:1907.07323 (2019)","DOI":"10.18653\/v1\/P19-2034"},{"key":"20_CR8","doi-asserted-by":"crossref","unstructured":"Zheng, H., Lapata, M.: Sentence centrality revisited for unsupervised summarization. arXiv preprint arXiv:1906.03508 (2019)","DOI":"10.18653\/v1\/P19-1628"},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Srivastava, R., et al.: A topic modeled unsupervised approach to single document extractive text summarization. Knowl.-Based Syst. 246, 108636 (2022)","DOI":"10.1016\/j.knosys.2022.108636"},{"key":"20_CR10","doi-asserted-by":"crossref","unstructured":"Tang, P., Hu, K., Yan, R., Zhang, L., Gao, J., Wang, Z.: OTExtSum: extractive text summarisation with optimal transport. arXiv preprint arXiv:2204.10086 (2022)","DOI":"10.18653\/v1\/2022.findings-naacl.85"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Maynez, J., Narayan, S., Bohnet, B., McDonald, R.: On faithfulness and factuality in abstractive summarization. arXiv preprint arXiv:2005.00661 (2020)","DOI":"10.18653\/v1\/2020.acl-main.173"},{"key":"20_CR12","doi-asserted-by":"crossref","unstructured":"Tanfouri, I., Jarray, F.: GaSUM: a genetic algorithm wrapped BERT for text summarization. In: International Conference on Agents and Artificial Intelligence (2023)","DOI":"10.5220\/0011893000003393"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"Tanfouri, T.G., Jarray, F.: An automatic Arabic text summarization system based on genetic algorithms. Procedia Comput. Sci. 189, 195\u2013202 (2021)","DOI":"10.1016\/j.procs.2021.05.083"},{"key":"20_CR14","doi-asserted-by":"crossref","unstructured":"Tanfouri, I., Jarray, F.: Genetic Algorithm and Latent Semantic Analysis based Documents Summarization Technique (2022)","DOI":"10.5220\/0011585700003335"},{"key":"20_CR15","unstructured":"Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. Adv. Neural Inf. Process. Syst. 26 (2013)"},{"key":"20_CR16","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Cohan, A., et al.: A discourse-aware attention model for abstractive summarization of long documents. arXiv preprint arXiv:1804.05685 (2018)","DOI":"10.18653\/v1\/N18-2097"},{"key":"20_CR18","unstructured":"Paulus, R., Xiong, C., Socher, R. : A deep reinforced model for abstractive summarization. arXiv preprint arXiv:1705.04304 (2017)"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Bai, Y., Gao, Y., Huang, H.: Cross-lingual abstractive summarization with limited parallel resources. arXiv preprint arXiv:2105.13648 (2021)","DOI":"10.18653\/v1\/2021.acl-long.538"},{"key":"20_CR20","unstructured":"Liu, Y.: Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318 (2019)"},{"key":"20_CR21","doi-asserted-by":"crossref","unstructured":"Nallapati, R., Zhou, B., Gulcehre, C., Xiang, B.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. arXiv preprint arXiv:1602.06023 (2016)","DOI":"10.18653\/v1\/K16-1028"}],"container-title":["Communications in Computer and Information Science","Advances in Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-41774-0_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T06:33:05Z","timestamp":1695277985000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-41774-0_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031417733","9783031417740"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-41774-0_20","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCCI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Collective Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Budapest","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hungary","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 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"218","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":"59","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":"27% - 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":"3.01","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":"1.86","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)"}}]}}