{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:06:43Z","timestamp":1764785203709,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031441974"},{"type":"electronic","value":"9783031441981"}],"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\/978-3-031-44198-1_6","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T08:02:34Z","timestamp":1695283354000},"page":"63-74","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["An Efficient Approach for\u00a0Improving the\u00a0Recall of\u00a0Rough Abstract Retrieval in\u00a0Scientific Claim Verification"],"prefix":"10.1007","author":[{"given":"Zhiwei","family":"Zhang","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4997-3850","authenticated-orcid":false,"given":"Jiyi","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7858-6206","authenticated-orcid":false,"given":"Fumiyo","family":"Fukumoto","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., Koyama, M.: Optuna: a next-generation hyperparameter optimization framework. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, pp. 2623\u20132631. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3292500.3330701","DOI":"10.1145\/3292500.3330701"},{"key":"6_CR2","doi-asserted-by":"publisher","unstructured":"Chen, J., Zhang, R., Guo, J., Fan, Y., Cheng, X.: Gere: generative evidence retrieval for fact verification. In: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2022, pp. 2184\u20132189. Association for Computing Machinery, New York (2022). https:\/\/doi.org\/10.1145\/3477495.3531827","DOI":"10.1145\/3477495.3531827"},{"key":"6_CR3","doi-asserted-by":"publisher","unstructured":"Chen, Q., Peng, Y., Lu, Z.: BioSentVec: creating sentence embeddings for biomedical texts. In: 2019 IEEE International Conference on Healthcare Informatics (ICHI), pp. 1\u20135 (2019). https:\/\/doi.org\/10.1109\/ICHI.2019.8904728","DOI":"10.1109\/ICHI.2019.8904728"},{"key":"6_CR4","doi-asserted-by":"crossref","unstructured":"Ferreira, W., Vlachos, A.: Emergent: a novel data-set for stance classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 1163\u20131168 (2016)","DOI":"10.18653\/v1\/N16-1138"},{"key":"6_CR5","doi-asserted-by":"crossref","unstructured":"Hanselowski, A., et al.: UKP-Athene: multi-sentence textual entailment for claim verification. In: Proceedings of the First Workshop on Fact Extraction and VERification (FEVER), pp. 103\u2013108 (2018)","DOI":"10.18653\/v1\/W18-5516"},{"key":"6_CR6","doi-asserted-by":"publisher","unstructured":"Hidey, C., et al.: DeSePtion: dual sequence prediction and adversarial examples for improved fact-checking. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8593\u20138606. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.761. https:\/\/www.aclweb.org\/anthology\/2020.acl-main.761","DOI":"10.18653\/v1\/2020.acl-main.761"},{"key":"6_CR7","unstructured":"Hinton, G.E., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. CoRR abs\/1503.02531 (2015). http:\/\/arxiv.org\/abs\/1503.02531"},{"issue":"4","key":"6_CR8","doi-asserted-by":"publisher","first-page":"1234","DOI":"10.1093\/bioinformatics\/btz682","volume":"36","author":"J Lee","year":"2019","unstructured":"Lee, J., et al.: BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36(4), 1234\u20131240 (2019). https:\/\/doi.org\/10.1093\/bioinformatics\/btz682","journal-title":"Bioinformatics"},{"key":"6_CR9","unstructured":"Li, X., Burns, G.A., Peng, N.: A paragraph-level multi-task learning model for scientific fact-verification. In: Veyseh, A.P.B., Dernoncourt, F., Nguyen, T.H., Chang, W., Celi, L.A. (eds.) Proceedings of the Workshop on Scientific Document Understanding co-located with 35th AAAI Conference on Artificial Intelligence, SDU@AAAI 2021, Virtual Event, 9 February 2021. CEUR Workshop Proceedings, vol. 2831. CEUR-WS.org (2021). http:\/\/ceur-ws.org\/Vol-2831\/paper8.pdf"},{"key":"6_CR10","unstructured":"Liu, Y., et al.: RoBERTa: a robustly optimized BERT pretraining approach. CoRR abs\/1907.11692 (2019). http:\/\/arxiv.org\/abs\/1907.11692"},{"key":"6_CR11","doi-asserted-by":"publisher","unstructured":"Liu, Z., Xiong, C., Sun, M., Liu, Z.: Fine-grained fact verification with kernel graph attention network. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7342\u20137351. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.655. https:\/\/aclanthology.org\/2020.acl-main.655","DOI":"10.18653\/v1\/2020.acl-main.655"},{"key":"6_CR12","doi-asserted-by":"publisher","unstructured":"Lu, Y.J., Li, C.T.: GCAN: graph-aware co-attention networks for explainable fake news detection on social media. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 505\u2013514. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.48. https:\/\/www.aclweb.org\/anthology\/2020.acl-main.48","DOI":"10.18653\/v1\/2020.acl-main.48"},{"key":"6_CR13","doi-asserted-by":"crossref","unstructured":"Nie, Y., Chen, H., Bansal, M.: Combining fact extraction and verification with neural semantic matching networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 6859\u20136866 (2019)","DOI":"10.1609\/aaai.v33i01.33016859"},{"key":"6_CR14","unstructured":"Pradeep, R., Ma, X., Nogueira, R., Lin, J.: Scientific claim verification with VerT5erini. In: Proceedings of the 12th International Workshop on Health Text Mining and Information Analysis, pp. 94\u2013103. Association for Computational Linguistics, Online (2021). https:\/\/www.aclweb.org\/anthology\/2021.louhi-1.11"},{"key":"6_CR15","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21(140), 1\u201367 (2020). http:\/\/jmlr.org\/papers\/v21\/20-074.html"},{"key":"6_CR16","doi-asserted-by":"publisher","unstructured":"Thorne, J., Vlachos, A., Christodoulopoulos, C., Mittal, A.: FEVER: a large-scale dataset for fact extraction and VERification. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, New Orleans, Louisiana (Volume 1: Long Papers), pp. 809\u2013819. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/N18-1074. https:\/\/www.aclweb.org\/anthology\/N18-1074","DOI":"10.18653\/v1\/N18-1074"},{"key":"6_CR17","doi-asserted-by":"crossref","unstructured":"Vlachos, A., Riedel, S.: Fact checking: task definition and dataset construction. In: Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science, pp. 18\u201322 (2014)","DOI":"10.3115\/v1\/W14-2508"},{"key":"6_CR18","doi-asserted-by":"publisher","unstructured":"Wadden, D., et al.: Fact or fiction: verifying scientific claims. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 7534\u20137550. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.609. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.609","DOI":"10.18653\/v1\/2020.emnlp-main.609"},{"key":"6_CR19","doi-asserted-by":"publisher","unstructured":"Wadden, D., Lo, K., Wang, L., Cohan, A., Beltagy, I., Hajishirzi, H.: MultiVerS: improving scientific claim verification with weak supervision and full-document context. In: Findings of the Association for Computational Linguistics: NAACL 2022, Seattle, USA, pp. 61\u201376. Association for Computational Linguistics (2022). https:\/\/doi.org\/10.18653\/v1\/2022.findings-naacl.6. https:\/\/aclanthology.org\/2022.findings-naacl.6","DOI":"10.18653\/v1\/2022.findings-naacl.6"},{"key":"6_CR20","doi-asserted-by":"publisher","unstructured":"Yang, Z., Yang, D., Dyer, C., He, X., Smola, A., Hovy, E.: Hierarchical attention networks for document classification. In: Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, San Diego, California, pp. 1480\u20131489. Association for Computational Linguistics (2016). https:\/\/doi.org\/10.18653\/v1\/N16-1174. https:\/\/aclanthology.org\/N16-1174","DOI":"10.18653\/v1\/N16-1174"},{"key":"6_CR21","doi-asserted-by":"publisher","unstructured":"Zeng, X., Zubiaga, A.: QMUL-SDS at SCIVER: step-by-step binary classification for scientific claim verification. In: Proceedings of the Second Workshop on Scholarly Document Processing, pp. 116\u2013123. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.sdp-1.15. https:\/\/aclanthology.org\/2021.sdp-1.15","DOI":"10.18653\/v1\/2021.sdp-1.15"},{"key":"6_CR22","doi-asserted-by":"publisher","unstructured":"Zhang, Z., Li, J., Fukumoto, F., Ye, Y.: Abstract, rationale, stance: a joint model for scientific claim verification. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, Punta Cana, Dominican Republic, pp. 3580\u20133586. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-main.290. https:\/\/aclanthology.org\/2021.emnlp-main.290","DOI":"10.18653\/v1\/2021.emnlp-main.290"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44198-1_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,28]],"date-time":"2023-11-28T22:11:13Z","timestamp":1701209473000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44198-1_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031441974","9783031441981"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44198-1_6","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":"22 September 2023","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":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 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":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted  : 9 Abstract","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)"}}]}}