{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T09:44:36Z","timestamp":1770284676799,"version":"3.49.0"},"publisher-location":"Cham","reference-count":42,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030997359","type":"print"},{"value":"9783030997366","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[[2022]]},"DOI":"10.1007\/978-3-030-99736-6_2","type":"book-chapter","created":{"date-parts":[[2022,4,4]],"date-time":"2022-04-04T23:02:47Z","timestamp":1649113367000},"page":"19-34","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["PARM: A Paragraph Aggregation Retrieval Model for\u00a0Dense Document-to-Document Retrieval"],"prefix":"10.1007","author":[{"given":"Sophia","family":"Althammer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sebastian","family":"Hofst\u00e4tter","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mete","family":"Sertkan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suzan","family":"Verberne","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Allan","family":"Hanbury","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,4,5]]},"reference":[{"key":"2_CR1","doi-asserted-by":"publisher","unstructured":"Abolghasemi, A., Verberne, S., Azzopardi, L.: Improving BERT-based query-by-document retrieval with multi-task optimization. In: Hagen, M. et al. (Eds.) ECIR 2022. LNCS, vol. 13185, pp. xx\u2013yy. Springer, Heidelberg (2022). https:\/\/doi.org\/10.1007\/978-3-030-99736-6_2","DOI":"10.1007\/978-3-030-99736-6_2"},{"key":"2_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1007\/978-3-319-76941-7_41","volume-title":"Advances in Information Retrieval","author":"Q Ai","year":"2018","unstructured":"Ai, Q., O\u2019Connor, B., Croft, W.B.: A neural passage model for ad-hoc document retrieval. In: Pasi, G., Piwowarski, B., Azzopardi, L., Hanbury, A. (eds.) ECIR 2018. LNCS, vol. 10772, pp. 537\u2013543. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-76941-7_41"},{"key":"2_CR3","doi-asserted-by":"publisher","unstructured":"Akkalyoncu Yilmaz, Z., Wang, S., Yang, W., Zhang, H., Lin, J.: Applying BERT to document retrieval with birch. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP): System Demonstrations, Hong Kong, China, November 2019, pp. 19\u201324. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-3004. https:\/\/aclanthology.org\/D19-3004","DOI":"10.18653\/v1\/D19-3004"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Akkalyoncu Yilmaz, Z., Yang, W., Zhang, H., Lin, J.: Cross-domain modeling of sentence-level evidence for document retrieval. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, November 2019, pp. 3490\u20133496. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1352. https:\/\/aclanthology.org\/D19-1352","DOI":"10.18653\/v1\/D19-1352"},{"key":"2_CR5","doi-asserted-by":"publisher","unstructured":"Akkalyoncu Yilmaz, Z., Yang, W., Zhang, H., Lin, J.: Cross-domain modeling of sentence-level evidence for document retrieval. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Hong Kong, China, November 2019, pp. 3490\u20133496. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1352. https:\/\/www.aclweb.org\/anthology\/D19-1352","DOI":"10.18653\/v1\/D19-1352"},{"key":"2_CR6","unstructured":"Bajaj, P., et al.: MS MARCO: a human generated MAchine Reading COmprehension dataset. In: Proceedings of the NIPS (2016)"},{"key":"2_CR7","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"162","DOI":"10.1007\/978-3-540-78646-7_17","volume-title":"Advances in Information Retrieval","author":"M Bendersky","year":"2008","unstructured":"Bendersky, M., Kurland, O.: Utilizing passage-based language models for document retrieval. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 162\u2013174. Springer, Heidelberg (2008). https:\/\/doi.org\/10.1007\/978-3-540-78646-7_17"},{"key":"2_CR8","doi-asserted-by":"publisher","unstructured":"Chalkidis, I., Fergadiotis, M., Malakasiotis, P., Aletras, N., Androutsopoulos, I.: LEGAL-BERT: the Muppets straight out of law school. In: Findings of the Association for Computational Linguistics, EMNLP 2020, Online, November 2020, pp. 2898\u20132904. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.261. https:\/\/www.aclweb.org\/anthology\/2020.findings-emnlp.261","DOI":"10.18653\/v1\/2020.findings-emnlp.261"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Cohan, A., Feldman, S., Beltagy, I., Downey, D., Weld, D.: SPECTER: document-level representation learning using citation-informed transformers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Online, July 2020, pp. 2270\u20132282. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.207. https:\/\/aclanthology.org\/2020.acl-main.207","DOI":"10.18653\/v1\/2020.acl-main.207"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Cormack, G.V., Clarke, C.L.A., Buettcher, S.: Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In: Proceedings of the 32nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2009, pp. 758\u2013759. Association for Computing Machinery, New York (2009). https:\/\/doi.org\/10.1145\/1571941.1572114","DOI":"10.1145\/1571941.1572114"},{"key":"2_CR11","doi-asserted-by":"publisher","unstructured":"Dai, Z., Callan, J.: Deeper text understanding for IR with contextual neural language modeling. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 985\u2013988. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3331184.3331303","DOI":"10.1145\/3331184.3331303"},{"key":"2_CR12","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: 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, June 2019, pp. 4171\u20134186. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/N19-1423. https:\/\/www.aclweb.org\/anthology\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"2_CR13","unstructured":"Gao, J., et al.: FIRE 2019@AILA: legal retrieval based on information retrieval model. In: Proceedings of the Forum for Information Retrieval Evaluation, FIRE 2019 (2019)"},{"key":"2_CR14","doi-asserted-by":"crossref","unstructured":"Gao, L., Dai, Z., Chen, T., Fan, Z., Durme, B.V., Callan, J.: Complementing lexical retrieval with semantic residual embedding. arXiv arXiv:2004.13969 (April 2020)","DOI":"10.1007\/978-3-030-72113-8_10"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"Hedin, B., Zaresefat, S., Baron, J., Oard, D.: Overview of the TREC 2009 legal track. In: Proceedings of the 18th Text REtrieval Conference, TREC 2009 (January 2009)","DOI":"10.6028\/NIST.SP.500-278.legal-overview"},{"key":"2_CR16","doi-asserted-by":"crossref","unstructured":"Garc\u00eda Seco de Herrera, A., Schaer, R., Markonis, D., M\u00fcller, H.: Comparing fusion techniques for the ImageCLEF 2013 medical case retrieval task. Comput. Med. Imaging Graph. 39, 46\u201354 (2014). http:\/\/publications.hevs.ch\/index.php\/attachments\/single\/676","DOI":"10.1016\/j.compmedimag.2014.04.004"},{"key":"2_CR17","unstructured":"Hofst\u00e4tter, S., Althammer, S., Schr\u00f6der, M., Sertkan, M., Hanbury, A.: Improving efficient neural ranking models with cross-architecture knowledge distillation (2021)"},{"key":"2_CR18","doi-asserted-by":"crossref","unstructured":"Hofst\u00e4tter, S., Lin, S.C., Yang, J.H., Lin, J., Hanbury, A.: Efficiently teaching an effective dense retriever with balanced topic aware sampling (2021)","DOI":"10.1145\/3404835.3462891"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Karpukhin, V., et al.: Dense passage retrieval for open-domain question answering. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 6769\u20136781. Association for Computational Linguistics, Online, November 2020 (2020). https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.550. https:\/\/www.aclweb.org\/anthology\/2020.emnlp-main.550","DOI":"10.18653\/v1\/2020.emnlp-main.550"},{"key":"2_CR20","doi-asserted-by":"publisher","unstructured":"Lee, J.H.: Analyses of multiple evidence combination. SIGIR Forum 31(SI), 267\u2013276 (1997). https:\/\/doi.org\/10.1145\/278459.258587","DOI":"10.1145\/278459.258587"},{"key":"2_CR21","unstructured":"Li, C., Yates, A., MacAvaney, S., He, B., Sun, Y.: Parade: passage representation aggregation for document reranking. arXiv preprint arXiv:2008.09093 (2020)"},{"key":"2_CR22","doi-asserted-by":"publisher","unstructured":"Liu, X., Croft, W.B.: Passage retrieval based on language models. In: Proceedings of the 11th International Conference on Information and Knowledge Management, CIKM 2002, pp. 375\u2013382. Association for Computing Machinery, New York (2002). https:\/\/doi.org\/10.1145\/584792.584854","DOI":"10.1145\/584792.584854"},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Locke, D., Zuccon, G.: A test collection for evaluating legal case law search. In: 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2018, pp. 1261\u20131264. Association for Computing Machinery, Inc. (June 2018). https:\/\/doi.org\/10.1145\/3209978.3210161","DOI":"10.1145\/3209978.3210161"},{"key":"2_CR24","doi-asserted-by":"publisher","first-page":"181","DOI":"10.1007\/978-3-319-70145-5_14","volume-title":"Information Retrieval Technology","author":"D Locke","year":"2017","unstructured":"Locke, D., Zuccon, G., Scells, H.: Automatic query generation from legal texts for case law retrieval. In: Sung, W.-K., et al. (eds.) Information Retrieval Technology, pp. 181\u2013193. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-70145-5_14"},{"key":"2_CR25","doi-asserted-by":"crossref","unstructured":"Luan, Y., Eisenstein, J., Toutanova, K., Collins, M.: Sparse, dense, and attentional representations for text retrieval. arXiv preprint arXiv:2005.00181 (2020)","DOI":"10.1162\/tacl_a_00369"},{"key":"2_CR26","doi-asserted-by":"publisher","unstructured":"Montague, M., Aslam, J.A.: Condorcet fusion for improved retrieval. In: Proceedings of the 11th International Conference on Information and Knowledge Management, CIKM 2002, pp. 538\u2013548. Association for Computing Machinery, New York (2002). https:\/\/doi.org\/10.1145\/584792.584881","DOI":"10.1145\/584792.584881"},{"key":"2_CR27","doi-asserted-by":"publisher","unstructured":"Mour\u00e3o, A., Martins, F., Magalh\u00e3es, J.: Multimodal medical information retrieval with unsupervised rank fusion. Comput. Med. Imaging Graph. 39, 35\u201345 (2015). Medical visual information analysis and retrieval. https:\/\/doi.org\/10.1016\/j.compmedimag.2014.05.006. https:\/\/www.sciencedirect.com\/science\/article\/pii\/S0895611114000664","DOI":"10.1016\/j.compmedimag.2014.05.006"},{"key":"2_CR28","unstructured":"Piroi, F., Tait, J.: CLEF-IP 2010: retrieval experiments in the intellectual property domain. In: Proceedings of CLEF 2010 (2010)"},{"key":"2_CR29","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"34","DOI":"10.1007\/978-3-030-58790-1_3","volume-title":"New Frontiers in Artificial Intelligence","author":"J Rabelo","year":"2020","unstructured":"Rabelo, J., Kim, M.-Y., Goebel, R., Yoshioka, M., Kano, Y., Satoh, K.: A summary of the COLIEE 2019 competition. In: Sakamoto, M., Okazaki, N., Mineshima, K., Satoh, K. (eds.) JSAI-isAI 2019. LNCS (LNAI), vol. 12331, pp. 34\u201349. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58790-1_3"},{"key":"2_CR30","unstructured":"Risch, J., Alder, N., Hewel, C., Krestel, R.: PatentMatch: a dataset for matching patent claims & prior art (2020)"},{"key":"2_CR31","doi-asserted-by":"publisher","unstructured":"Robertson, S., Zaragoza, H.: The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. 3(4), 333\u2013389 (2009). https:\/\/doi.org\/10.1561\/1500000019","DOI":"10.1561\/1500000019"},{"key":"2_CR32","unstructured":"Shao, Y., Liu, B., Mao, J., Liu, Y., Zhang, M., Ma, S.: THUIR@COLIEE-2020: leveraging semantic understanding and exact matching for legal case retrieval and entailment. CoRR abs\/2012.13102 (2020). https:\/\/arxiv.org\/abs\/2012.13102"},{"key":"2_CR33","doi-asserted-by":"publisher","unstructured":"Shao, Y., et al.: BERT-PLI: modeling paragraph-level interactions for legal case retrieval. In: Bessiere, C. (ed.) Proceedings of the 29th International Joint Conference on Artificial Intelligence, IJCAI-20, pp. 3501\u20133507. International Joint Conferences on Artificial Intelligence Organization (July 2020). Main track. https:\/\/doi.org\/10.24963\/ijcai.2020\/484","DOI":"10.24963\/ijcai.2020\/484"},{"key":"2_CR34","doi-asserted-by":"crossref","unstructured":"Shaw, J.A., Fox, E.A.: Combination of multiple searches. In: The 2nd Text Retrieval Conference, TREC-2, pp. 243\u2013252 (1994)","DOI":"10.6028\/NIST.SP.500-225.vpi"},{"key":"2_CR35","doi-asserted-by":"publisher","unstructured":"Van\u00a0Opijnen, M., Santos, C.: On the concept of relevance in legal information retrieval. Artif. Intell. Law 25(1), 65\u201387 (2017). https:\/\/doi.org\/10.1007\/s10506-017-9195-8","DOI":"10.1007\/s10506-017-9195-8"},{"key":"2_CR36","doi-asserted-by":"publisher","unstructured":"Wu, S.: Ranking-based fusion. In: Data Fusion in Information Retrieval. Adaptation, Learning, and Optimization, vol. 13, pp 135\u2013147. Springer, Heidelberg (2012). https:\/\/doi.org\/10.1007\/978-3-642-28866-1_7","DOI":"10.1007\/978-3-642-28866-1_7"},{"key":"2_CR37","doi-asserted-by":"publisher","unstructured":"Wu, Z., et al.: Leveraging passage-level cumulative gain for document ranking. In: Proceedings of the Web Conference 2020, WWW 2020, pp. 2421\u20132431. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3366423.3380305","DOI":"10.1145\/3366423.3380305"},{"key":"2_CR38","doi-asserted-by":"publisher","unstructured":"Wu, Z., Mao, J., Liu, Y., Zhang, M., Ma, S.: Investigating passage-level relevance and its role in document-level relevance judgment. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2019, pp. 605\u2013614. Association for Computing Machinery, New York (2019). https:\/\/doi.org\/10.1145\/3331184.3331233","DOI":"10.1145\/3331184.3331233"},{"key":"2_CR39","unstructured":"Xiong, L., et al.: Approximate nearest neighbor negative contrastive learning for dense text retrieval. In: International Conference on Learning Representations (2021). https:\/\/openreview.net\/forum?id=zeFrfgyZln"},{"key":"2_CR40","doi-asserted-by":"publisher","unstructured":"Yang, L., Zhang, M., Li, C., Bendersky, M., Najork, M.: Beyond 512 tokens: Siamese multi-depth transformer-based hierarchical encoder for long-form document matching. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, CIKM 2020, pp. 1725\u20131734. Association for Computing Machinery, New York (2020). https:\/\/doi.org\/10.1145\/3340531.3411908","DOI":"10.1145\/3340531.3411908"},{"key":"2_CR41","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"150","DOI":"10.1007\/978-3-030-72240-1_11","volume-title":"Advances in Information Retrieval","author":"X Zhang","year":"2021","unstructured":"Zhang, X., Yates, A., Lin, J.: Comparing score aggregation approaches for document retrieval with pretrained transformers. In: Hiemstra, D., Moens, M.-F., Mothe, J., Perego, R., Potthast, M., Sebastiani, F. (eds.) ECIR 2021. LNCS, vol. 12657, pp. 150\u2013163. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-72240-1_11"},{"key":"2_CR42","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yates, A., Lin, J.J.: Comparing score aggregation approaches for document retrieval with pretrained transformers. In: ECIR (2021)","DOI":"10.1007\/978-3-030-72240-1_11"}],"container-title":["Lecture Notes in Computer Science","Advances in Information Retrieval"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-99736-6_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,21]],"date-time":"2024-09-21T15:41:34Z","timestamp":1726933294000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-99736-6_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783030997359","9783030997366"],"references-count":42,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-99736-6_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"5 April 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECIR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Information Retrieval","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Stavanger","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Norway","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 April 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 April 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecir2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecir2022.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-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":"395","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":"35","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":"29","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":"9% - 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":"4-6","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":"Additionally, there are other papers: 11 reproducibility, 12 doctoral, 13 CLEF Labs, 5 workshops and 4 tutorials.","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)"}}]}}