{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T02:38:03Z","timestamp":1743043083533,"version":"3.40.3"},"publisher-location":"Cham","reference-count":25,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031708152"},{"type":"electronic","value":"9783031708169"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-70816-9_21","type":"book-chapter","created":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T16:02:28Z","timestamp":1725552148000},"page":"263-276","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Refining Natural Language Inferences Using Cross-Document Structure Theory"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9203-5520","authenticated-orcid":false,"given":"Arkadiusz","family":"Janz","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3515-1359","authenticated-orcid":false,"given":"Dominik","family":"Kurowski","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6792-7028","authenticated-orcid":false,"given":"Joanna","family":"Baran","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8581-1098","authenticated-orcid":false,"given":"Julia","family":"Moska","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-3985-8656","authenticated-orcid":false,"given":"Tomasz","family":"Berna\u015b","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7740-5557","authenticated-orcid":false,"given":"Marcin","family":"Oleksy","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,8,28]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1007\/s10579-015-9332-5","volume":"50","author":"L Bentivogli","year":"2016","unstructured":"Bentivogli, L., Bernardi, R., Marelli, M., Menini, S., Baroni, M., Zamparelli, R.: SICK through the SemEval glasses. Lesson learned from the evaluation of compositional distributional semantic models on full sentences through semantic relatedness and textual entailment. Lang. Resour. Eval. 50, 95\u2013124 (2016)","journal-title":"Lang. Resour. Eval."},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Bowman, S.R., Angeli, G., Potts, C., Manning, C.D.: A large annotated corpus for learning natural language inference. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 632\u2013642. Association for Computational Linguistics (2015)","DOI":"10.18653\/v1\/D15-1075"},{"key":"21_CR3","unstructured":"Cardoso, P.C., et al.: CSTNews - a discourse-annotated corpus for single and multi-document summarization of news texts in Brazilian Portuguese. In: Proceedings of the 3rd RST Brazilian Meeting, Cuiab\u00e1, Brazil, pp. 88\u2013105 (2011)"},{"key":"21_CR4","unstructured":"Clark, C., Lee, K., Chang, M.W., Kwiatkowski, T., Collins, M., Toutanova, K.: BoolQ: exploring the surprising difficulty of natural yes\/no questions. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long and Short Papers), Minneapolis, Minnesota, pp. 2924\u20132936. Association for Computational Linguistics (2019)"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Conneau, A., et al.: Unsupervised cross-lingual representation learning at scale. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 8440\u20138451. Association for Computational Linguistics, Online (2020)","DOI":"10.18653\/v1\/2020.acl-main.747"},{"key":"21_CR6","unstructured":"Dolan, W.B., Brockett, C.: Automatically constructing a corpus of sentential paraphrases. In: Proceedings of the Third International Workshop on Paraphrasing (IWP2005) (2005). https:\/\/aclanthology.org\/I05-5002"},{"key":"21_CR7","unstructured":"Fyodorov, Y., Winter, Y., Francez, N.: A natural logic inference system. In: Proceedings of the 2nd Workshop on Inference in Computational Semantics (ICoS-2) (2000)"},{"key":"21_CR8","doi-asserted-by":"crossref","unstructured":"Glockner, M., Shwartz, V., Goldberg, Y.: Breaking NLI systems with sentences that require simple lexical inferences. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), Melbourne, Australia, pp. 650\u2013655. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/P18-2103"},{"key":"21_CR9","unstructured":"Gupta, V., Venkit, P.N., Lauren\u00e7on, H., Wilson, S., Passonneau, R.J.: Calm: a multi-task benchmark for comprehensive assessment of language model bias. arXiv preprint arXiv:2308.12539 (2023)"},{"key":"21_CR10","unstructured":"Humeau, S., Shuster, K., Lachaux, M.A., Weston, J.: Poly-encoders: architectures and pre-training strategies for fast and accurate multi-sentence scoring. In: International Conference on Learning Representations (2020)"},{"key":"21_CR11","unstructured":"Janz, A., Piasecki, M., W\u0105torski, P.: Neural language models vs Wordnet-based semantically enriched representation in CST relation recognition. In: Proceedings of the 11th Global Wordnet Conference, pp. 223\u2013233. Global Wordnet Association, University of South Africa (UNISA) (2021)"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"K\u0119dzia, P., Piasecki, M., Janz, A.: Graph-based approach to recognizing CST relations in Polish texts. In: Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, Varna, Bulgaria, pp. 363\u2013371. INCOMA Ltd. (2017)","DOI":"10.26615\/978-954-452-049-6_048"},{"key":"21_CR13","doi-asserted-by":"crossref","unstructured":"Khot, T., Sabharwal, A., Clark, P.: Scitail: a textual entailment dataset from science question answering. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol.\u00a032 (2018)","DOI":"10.1609\/aaai.v32i1.12022"},{"key":"21_CR14","doi-asserted-by":"crossref","unstructured":"Koco\u0144, J., Mi\u0142kowski, P., Za\u015bko-Zieli\u0144ska, M.: Multi-level sentiment analysis of PolEmo 2.0: extended corpus of multi-domain consumer reviews. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), Hong Kong, China, pp. 980\u2013991. Association for Computational Linguistics (2019)","DOI":"10.18653\/v1\/K19-1092"},{"key":"21_CR15","unstructured":"Marcinczuk, M., Ptak, M., Radziszewski, A., Piasecki, M.: Open dataset for development of polish question answering systems. In: Proceedings of the 6th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Wydawnictwo Poznanskie, Fundacja Uniwersytetu im. Adama Mickiewicza (2013)"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Maziero, E.G., del Ros\u00e1rio Castro\u00a0Jorge, M.L., Pardo, T.A.S.: Revisiting cross-document structure theory for multi-document discourse parsing. Inf. Process. Manag. 50(2), 297\u2013314 (2014)","DOI":"10.1016\/j.ipm.2013.12.003"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Radev, D.R.: A common theory of information fusion from multiple text sources step one: cross-document structure. In: Proceedings of the 1st SIGdial Workshop on Discourse and Dialogue, SIGDIAL 2000, Stroudsburg, PA, USA, vol. 10, pp. 74\u201383. Association for Computational Linguistics (2000)","DOI":"10.3115\/1117736.1117745"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Ratnayaka, G., Rupasinghe, T., de\u00a0Silva, N., Warushavithana, M., Gamage, V., Perera, A.S.: Identifying relationships among sentences in court case transcripts using discourse relations. In: 2018 18th International Conference on Advances in ICT for Emerging Regions (ICTer), pp. 13\u201320. IEEE (2018)","DOI":"10.1109\/ICTER.2018.8615485"},{"key":"21_CR19","doi-asserted-by":"crossref","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 (Volume 1: Long Papers), New Orleans, Louisiana, pp. 809\u2013819. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/N18-1074"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"V\u00edta, M.: From machine translated NLI corpus to universal sentence representations in Czech. In: FedCSIS (Position Papers), pp.\u00a03\u20138 (2020)","DOI":"10.15439\/2020F212"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Wang, A., Singh, A., Michael, J., Hill, F., Levy, O., Bowman, S.: GLUE: a multi-task benchmark and analysis platform for natural language understanding. In: Linzen, T., Chrupa\u0142a, G., Alishahi, A. (eds.) Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, Brussels, Belgium, pp. 353\u2013355. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/W18-5446"},{"key":"21_CR22","doi-asserted-by":"crossref","unstructured":"Williams, A., Nangia, N., Bowman, S.: A broad-coverage challenge corpus for sentence understanding through inference. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 1112\u20131122. Association for Computational Linguistics (2018)","DOI":"10.18653\/v1\/N18-1101"},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Wr\u00f3blewska, A., Krasnowska-Kiera\u015b, K.: Polish evaluation dataset for compositional distributional semantics models. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Vancouver, Canada, pp. 784\u2013792. Association for Computational Linguistics (2017)","DOI":"10.18653\/v1\/P17-1073"},{"key":"21_CR24","unstructured":"Zhang, Z., Blair-Goldensohn, S., Radev, D.R.: Towards CST-enhanced summarization. In: Proceedings of the Eighteenth National Conference on Artificial Intelligence and Fourteenth Conference on Innovative Applications of Artificial Intelligence, pp. 439\u2013446. AAAI Press\/The MIT Press (2002)"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Otterbacher, J., Radev, D.: Learning cross-document structural relationships using boosting. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, CIKM 2003, pp. 124\u2013130. ACM, New York (2003)","DOI":"10.1145\/956863.956887"}],"container-title":["Lecture Notes in Computer Science","Computational Collective Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-70816-9_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T16:06:53Z","timestamp":1725552413000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-70816-9_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031708152","9783031708169"],"references-count":25,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-70816-9_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"28 August 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"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":"Leipzig","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 September 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 September 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccci2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iccci.pwr.edu.pl\/2024\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}