{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T11:13:06Z","timestamp":1742987586370,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":25,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819981472"},{"type":"electronic","value":"9789819981489"}],"license":[{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,26]],"date-time":"2023-11-26T00:00:00Z","timestamp":1700956800000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-8148-9_16","type":"book-chapter","created":{"date-parts":[[2023,11,25]],"date-time":"2023-11-25T10:02:23Z","timestamp":1700906543000},"page":"198-210","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-granularity Contrastive Siamese Networks for\u00a0Abstractive Text Summarization"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0912-4870","authenticated-orcid":false,"given":"Hu","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Kunrui","family":"Li","sequence":"additional","affiliation":[]},{"given":"Guangjun","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Yong","family":"Guan","sequence":"additional","affiliation":[]},{"given":"Ru","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,26]]},"reference":[{"key":"16_CR1","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: Bart: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. arXiv preprint arXiv:1910.13461 (2019)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"16_CR2","unstructured":"Zhang, J., Zhao, Y., Saleh, M., Liu, P.: Pegasus: pre-training with extracted gap-sentences for abstractive summarization. In: International Conference on Machine Learning, pp. 11328\u201311339. PMLR. (2020)"},{"key":"16_CR3","doi-asserted-by":"crossref","unstructured":"Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. arXiv preprint arXiv:1707.07328 (2017)","DOI":"10.18653\/v1\/D17-1215"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Zheng, C., Zhang, K., Wang, H. J., Fan, L., Wang, Z.: Enhanced Seq2Seq autoencoder via contrastive learning for abstractive text summarization. In: 2021 IEEE International Conference on Big Data (Big Data), pp. 1764\u20131771. IEEE. (2021)","DOI":"10.1109\/BigData52589.2021.9671819"},{"key":"16_CR5","doi-asserted-by":"crossref","unstructured":"Zhou, K., Zhang, B., Zhao, W. X., Wen, J. R.: Debiased contrastive learning of unsupervised sentence representations. arXiv preprint arXiv:2205.00656 (2022)","DOI":"10.18653\/v1\/2022.acl-long.423"},{"key":"16_CR6","unstructured":"Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning, pp. 1597\u20131607. PMLR. (2020)"},{"key":"16_CR7","doi-asserted-by":"crossref","unstructured":"He, K., Fan, H., Wu, Y., Xie, S., Girshick, R.: Momentum contrast for unsupervised visual representation learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9729\u20139738 (2020)","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"16_CR8","unstructured":"Grill, J.B., et al.: Bootstrap your own latent-a new approach to self-supervised learning. In: Advances in Neural Information Processing Systems, vol. 33, 21271\u201321284 (2020)"},{"key":"16_CR9","unstructured":"Lee, S., Lee, D.B., Hwang, S.J.: Contrastive learning with adversarial perturbations for conditional text generation. arXiv preprint arXiv:2012.07280 (2020)"},{"key":"16_CR10","doi-asserted-by":"crossref","unstructured":"Liu, Y., Liu, P.: SimCLS: a simple framework for contrastive learning of abstractive summarization. arXiv preprint arXiv:2106.01890 (2021)","DOI":"10.18653\/v1\/2021.acl-short.135"},{"key":"16_CR11","unstructured":"Sun, S., Li, W.: Alleviating exposure bias via contrastive learning for abstractive text summarization. arXiv preprint arXiv:2108.11846 (2021)"},{"key":"16_CR12","doi-asserted-by":"crossref","unstructured":"Gu, J., Lu, Z., Li, H., Li, V. O.: Incorporating copying mechanism in sequence-to-sequence learning. arXiv preprint arXiv:1603.06393 (2016)","DOI":"10.18653\/v1\/P16-1154"},{"key":"16_CR13","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. arXiv preprint arXiv:1704.04368 (2017)","DOI":"10.18653\/v1\/P17-1099"},{"key":"16_CR14","doi-asserted-by":"crossref","unstructured":"Li, H., et al.: Learn to copy from the copying history: correlational copy network for abstractive summarization. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp. 4091\u20134101 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.336"},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Qi, W., et al.: Prophetnet: predicting future n-gram for sequence-to-sequence pre-training. arXiv preprint arXiv:2001.04063 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.217"},{"key":"16_CR16","doi-asserted-by":"crossref","unstructured":"Gao, T., Yao, X., Chen, D.: Simcse: simple contrastive learning of sentence embeddings. arXiv preprint arXiv:2104.08821 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.552"},{"key":"16_CR17","doi-asserted-by":"crossref","unstructured":"Qiu, S., et al.: Easyaug: an automatic textual data augmentation platform for classification tasks. In: Companion Proceedings of the Web Conference 2020, pp. 249\u2013252 (2020)","DOI":"10.1145\/3366424.3383552"},{"key":"16_CR18","unstructured":"Hermann, K.M., et al.: Teaching machines to read and comprehend. In: Advances in Neural Information Processing Systems, vol. 28 (2015)"},{"key":"16_CR19","doi-asserted-by":"crossref","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. arXiv preprint arXiv:1808.08745 (2018)","DOI":"10.18653\/v1\/D18-1206"},{"key":"16_CR20","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":"16_CR21","unstructured":"Xie, Q., Huang, J., Saha, T., Ananiadou, S.: GRETEL: graph contrastive topic enhanced language model for long document extractive summarization. arXiv preprint arXiv:2208.09982 (2022)"},{"key":"16_CR22","doi-asserted-by":"crossref","unstructured":"Ji, X., Zhao, W.: SKGSUM: abstractive document summarization with semantic knowledge graphs. In: 2021 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE (2021)","DOI":"10.1109\/IJCNN52387.2021.9533494"},{"key":"16_CR23","doi-asserted-by":"crossref","unstructured":"Qiu, Y., Cohen, S.B.: Abstractive summarization guided by latent hierarchical document structure. arXiv preprint arXiv:2211.09458 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.355"},{"key":"16_CR24","unstructured":"Liu, W., Wu, H., Mu, W., Li, Z., Chen, T., Nie, D.: CO2Sum: contrastive learning for factual-consistent abstractive summarization. arXiv preprint arXiv:2112.01147 (2021)"},{"key":"16_CR25","doi-asserted-by":"crossref","unstructured":"Xu, S., Zhang, X., Wu, Y., Wei, F.: Sequence level contrastive learning for text summarization. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, no. 10, pp. 11556\u201311565 (2022)","DOI":"10.1609\/aaai.v36i10.21409"}],"container-title":["Communications in Computer and Information Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8148-9_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T18:36:50Z","timestamp":1710268610000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8148-9_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,26]]},"ISBN":["9789819981472","9789819981489"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8148-9_16","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023,11,26]]},"assertion":[{"value":"26 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Changsha","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":"20 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 November 2023","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":"iconip2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2023.org\/","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":"1274","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":"650","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":"51% - 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":"4.14","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":"2.46","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)"}}]}}