{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T14:47:36Z","timestamp":1773326856527,"version":"3.50.1"},"publisher-location":"Singapore","reference-count":45,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819980871","type":"print"},{"value":"9789819980888","type":"electronic"}],"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\/978-981-99-8088-8_22","type":"book-chapter","created":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T05:02:07Z","timestamp":1701234127000},"page":"250-267","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["MuP-SciDocSum: Leveraging Multi-perspective Peer Review Summaries for\u00a0Scientific Document Summarization"],"prefix":"10.1007","author":[{"given":"Sandeep","family":"Kumar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guneet Singh","family":"Kohli","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tirthankar","family":"Ghosal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Asif","family":"Ekbal","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,30]]},"reference":[{"key":"22_CR1","unstructured":"Akkasi, A.: Multi perspective scientific document summarization with graph attention networks (GATS). In: Proceedings of the Third Workshop on Scholarly Document Processing, pp. 268\u2013272. Association for Computational Linguistics, Gyeongju (2022). https:\/\/aclanthology.org\/2022.sdp-1.33"},{"key":"22_CR2","doi-asserted-by":"publisher","unstructured":"Arora, H., Ghosal, T., Kumar, S., Patwal, S., Gooch, P.: INNOVATORS at SemEval-2021 task-11: a dependency parsing and bert-based model for extracting contribution knowledge from scientific papers. In: Palmer, A., Schneider, N., Schluter, N., Emerson, G., Herbelot, A., Zhu, X. (eds.) Proceedings of the 15th International Workshop on Semantic Evaluation, SemEval@ACL\/IJCNLP 2021, Virtual Event \/ Bangkok, Thailand, 5\u20136 August 2021, pp. 502\u2013510. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.61","DOI":"10.18653\/v1\/2021.semeval-1.61"},{"key":"22_CR3","doi-asserted-by":"publisher","unstructured":"Auer, S., et al.: Improving access to scientific literature with knowledge graphs. Bibliothek Forschung Praxis 44(3), 516\u2013529 (2020). https:\/\/doi.org\/10.1515\/bfp-2020-2042","DOI":"10.1515\/bfp-2020-2042"},{"key":"22_CR4","doi-asserted-by":"publisher","unstructured":"Cao, M.: A survey on neural abstractive summarization methods and factual consistency of summarization. CoRR abs\/2204.09519 (2022). https:\/\/doi.org\/10.48550\/arXiv.2204.09519","DOI":"10.48550\/arXiv.2204.09519"},{"key":"22_CR5","doi-asserted-by":"publisher","unstructured":"Cao, Z., Wei, F., Li, S., Li, W., Zhou, M., Wang, H.: Learning summary prior representation for extractive summarization. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pp. 829\u2013833. Association for Computational Linguistics, Beijing (2015). https:\/\/doi.org\/10.3115\/v1\/P15-2136, https:\/\/aclanthology.org\/P15-2136","DOI":"10.3115\/v1\/P15-2136"},{"key":"22_CR6","doi-asserted-by":"publisher","unstructured":"Chandrasekaran, M.K., Feigenblat, G., Hovy, E.H., Ravichander, A., Shmueli-Scheuer, M., de\u00a0Waard, A.: Overview and insights from the shared tasks at scholarly document processing 2020: CL-SciSumm, LaySumm and LongSumm. In: Chandrasekaran, M.K., et al. (eds.) Proceedings of the First Workshop on Scholarly Document Processing, SDP@EMNLP 2020, Online, 19 November 2020, pp. 214\u2013224. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.sdp-1.24","DOI":"10.18653\/v1\/2020.sdp-1.24"},{"key":"22_CR7","doi-asserted-by":"publisher","unstructured":"Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 484\u2013494. Association for Computational Linguistics, Berlin (2016). https:\/\/doi.org\/10.18653\/v1\/P16-1046, https:\/\/aclanthology.org\/P16-1046","DOI":"10.18653\/v1\/P16-1046"},{"key":"22_CR8","unstructured":"Chung, J., G\u00fcl\u00e7ehre, \u00c7., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. CoRR abs\/1412.3555 (2014). http:\/\/arxiv.org\/abs\/1412.3555"},{"key":"22_CR9","doi-asserted-by":"publisher","unstructured":"Cohan, A., et al.: A discourse-aware attention model for abstractive summarization of long documents. In: Walker, M.A., Ji, H., Stent, A. (eds.) Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT, New Orleans, Louisiana, USA, 1\u20136 June 2018, Volume 2 (Short Papers), pp. 615\u2013621. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/n18-2097","DOI":"10.18653\/v1\/n18-2097"},{"key":"22_CR10","unstructured":"Cohan, A., Feigenblat, G., Ghosal, T., Shmueli-Scheuer, M.: Overview of the first shared task on multi perspective scientific document summarization (MuP). In: Cohan, A., et al. (eds.) Proceedings of the Third Workshop on Scholarly Document Processing, SDP@COLING 2022, Gyeongju, Republic of Korea, 12\u201317 October 2022, pp. 263\u2013267. Association for Computational Linguistics (2022). https:\/\/aclanthology.org\/2022.sdp-1.32"},{"issue":"2\u20133","key":"22_CR11","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1007\/s00799-017-0216-8","volume":"19","author":"A Cohan","year":"2018","unstructured":"Cohan, A., Goharian, N.: Scientific document summarization via citation contextualization and scientific discourse. Int. J. Digit. Libr. 19(2\u20133), 287\u2013303 (2018). https:\/\/doi.org\/10.1007\/s00799-017-0216-8","journal-title":"Int. J. Digit. Libr."},{"key":"22_CR12","doi-asserted-by":"publisher","unstructured":"D\u2019Souza, J., Auer, S., Pedersen, T.: SemEval-2021 task 11: NLPContributionGraph - structuring scholarly NLP contributions for a research knowledge graph. In: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pp. 364\u2013376. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.44, https:\/\/aclanthology.org\/2021.semeval-1.44","DOI":"10.18653\/v1\/2021.semeval-1.44"},{"issue":"1","key":"22_CR13","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1002\/asi.20707","volume":"59","author":"A Elkiss","year":"2008","unstructured":"Elkiss, A., Shen, S., Fader, A., Erkan, G., States, D.J., Radev, D.R.: Blind men and elephants: what do citation summaries tell us about a research article? J. Assoc. Inf. Sci. Technol. 59(1), 51\u201362 (2008). https:\/\/doi.org\/10.1002\/asi.20707","journal-title":"J. Assoc. Inf. Sci. Technol."},{"key":"22_CR14","doi-asserted-by":"publisher","unstructured":"Erera, S., et al.: A summarization system for scientific documents. In: Pad\u00f3, S., Huang, R. (eds.) Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, 3\u20137 November 2019 - System Demonstrations, pp. 211\u2013216. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-3036","DOI":"10.18653\/v1\/D19-3036"},{"key":"22_CR15","unstructured":"Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. CoRR abs\/1109.2128 (2011). http:\/\/arxiv.org\/abs\/1109.2128"},{"key":"22_CR16","doi-asserted-by":"publisher","unstructured":"Ghosh\u00a0Roy, S., Pinnaparaju, N., Jain, R., Gupta, M., Varma, V.: Summaformers @ LaySumm 20, LongSumm 20. In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 336\u2013343. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.sdp-1.39, https:\/\/aclanthology.org\/2020.sdp-1.39","DOI":"10.18653\/v1\/2020.sdp-1.39"},{"key":"22_CR17","doi-asserted-by":"publisher","unstructured":"Gidiotis, A., Stefanidis, S., Tsoumakas, G.: AUTH @ CLSciSumm 20, LaySumm 20, LongSumm 20. In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 251\u2013260. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.sdp-1.28, https:\/\/aclanthology.org\/2020.sdp-1.28","DOI":"10.18653\/v1\/2020.sdp-1.28"},{"key":"22_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1007\/978-3-030-91669-5_34","volume-title":"Towards Open and Trustworthy Digital Societies","author":"K Gupta","year":"2021","unstructured":"Gupta, K., Ahmad, A., Ghosal, T., Ekbal, A.: ContriSci: a\u00a0BERT-based multitasking deep neural architecture to\u00a0identify contribution statements from\u00a0research papers. In: Ke, H.-R., Lee, C.S., Sugiyama, K. (eds.) ICADL 2021. LNCS, vol. 13133, pp. 436\u2013452. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-91669-5_34"},{"key":"22_CR19","unstructured":"Harman, D., Over, P.: The effects of human variation in DUC summarization evaluation. In: Text Summarization Branches Out, pp. 10\u201317. Association for Computational Linguistics, Barcelona (2004). https:\/\/aclanthology.org\/W04-1003"},{"key":"22_CR20","unstructured":"Hermann, K.M., et al.: Teaching machines to read and comprehend. CoRR abs\/1506.03340 (2015). http:\/\/arxiv.org\/abs\/1506.03340"},{"key":"22_CR21","doi-asserted-by":"publisher","unstructured":"Hirsch, E., et al.: iFacetSum: coreference-based interactive faceted summarization for multi-document exploration. In: Adel, H., Shi, S. (eds.) Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, EMNLP 2021, Online and Punta Cana, Dominican Republic, 7\u201311 November 2021, pp. 283\u2013297. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.emnlp-demo.33","DOI":"10.18653\/v1\/2021.emnlp-demo.33"},{"key":"22_CR22","unstructured":"Jaidka, K., Kumar\u00a0Chandrasekaran, M., Rustagi, S., Kan, M.Y.: Overview of the CL-SciSumm 2016 shared task. In: Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL), pp. 93\u2013102 (2016). https:\/\/aclanthology.org\/W16-1511"},{"key":"22_CR23","doi-asserted-by":"publisher","unstructured":"Kim, S.: Using pre-trained transformer for better lay summarization. In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 328\u2013335. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.sdp-1.38, https:\/\/aclanthology.org\/2020.sdp-1.38","DOI":"10.18653\/v1\/2020.sdp-1.38"},{"key":"22_CR24","unstructured":"Kumar, S., Kohli, G.S., Shinde, K., Ekbal, A.: Team AINLPML @ MuP in SDP 2021: scientific document summarization by end-to-end extractive and abstractive approach. In: Proceedings of the Third Workshop on Scholarly Document Processing, pp. 285\u2013290. Association for Computational Linguistics, Gyeongju (2022). https:\/\/aclanthology.org\/2022.sdp-1.36"},{"key":"22_CR25","doi-asserted-by":"publisher","unstructured":"Lin, J., Ling, J., Wang, Z., Liu, J., Chen, Q., He, L.: ECNUICA at SemEval-2021 task 11: rule based information extraction pipeline. In: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pp. 1295\u20131302. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.185, https:\/\/aclanthology.org\/2021.semeval-1.185","DOI":"10.18653\/v1\/2021.semeval-1.185"},{"key":"22_CR26","doi-asserted-by":"publisher","unstructured":"Liu, H., Sarol, M.J., Kilicoglu, H.: UIUC_BioNLP at SemEval-2021 task 11: a cascade of neural models for structuring scholarly NLP contributions. In: Palmer, A., Schneider, N., Schluter, N., Emerson, G., Herbelot, A., Zhu, X. (eds.) Proceedings of the 15th International Workshop on Semantic Evaluation, SemEval@ACL\/IJCNLP 2021, Virtual Event\/Bangkok, Thailand, 5\u20136 August 2021, pp. 377\u2013386. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.45","DOI":"10.18653\/v1\/2021.semeval-1.45"},{"key":"22_CR27","doi-asserted-by":"publisher","unstructured":"Liu, Y., Ni, A., Nan, L., Deb, B., Zhu, C., Awadallah, A.H., Radev, D.R.: Leveraging locality in abstractive text summarization. CoRR abs\/2205.12476 (2022). https:\/\/doi.org\/10.48550\/arXiv.2205.12476","DOI":"10.48550\/arXiv.2205.12476"},{"key":"22_CR28","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1016\/j.datak.2013.08.005","volume":"88","author":"E Lloret","year":"2013","unstructured":"Lloret, E., Rom\u00e1-Ferri, M.T., Palomar, M.: COMPENDIUM: a text summarization system for generating abstracts of research papers. Data Knowl. Eng. 88, 164\u2013175 (2013). https:\/\/doi.org\/10.1016\/j.datak.2013.08.005","journal-title":"Data Knowl. Eng."},{"key":"22_CR29","doi-asserted-by":"publisher","unstructured":"Ma, X., Wang, J., Zhang, X.: YNU-HPCC at SemEval-2021 task 11: using a BERT model to extract contributions from NLP scholarly articles. In: Palmer, A., Schneider, N., Schluter, N., Emerson, G., Herbelot, A., Zhu, X. (eds.) Proceedings of the 15th International Workshop on Semantic Evaluation, SemEval@ACL\/IJCNLP 2021, Virtual Event\/Bangkok, Thailand, 5\u20136 August 2021, pp. 478\u2013484. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.58","DOI":"10.18653\/v1\/2021.semeval-1.58"},{"key":"22_CR30","doi-asserted-by":"publisher","unstructured":"Martin, A., Pedersen, T.: Duluth at SemEval-2021 task 11: applying deBERTa to contributing sentence selection and dependency parsing for entity extraction. In: Palmer, A., Schneider, N., Schluter, N., Emerson, G., Herbelot, A., Zhu, X. (eds.) Proceedings of the 15th International Workshop on Semantic Evaluation, SemEval@ACL\/IJCNLP 2021, Virtual Event\/Bangkok, Thailand, 5\u20136 August 2021, pp. 490\u2013501. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.60","DOI":"10.18653\/v1\/2021.semeval-1.60"},{"key":"22_CR31","doi-asserted-by":"publisher","unstructured":"Mishra, S.K., Kundarapu, H., Saini, N., Saha, S., Bhattacharyya, P.: IITP-AI-NLP-ML@ CL-SciSumm 2020, CL-LaySumm 2020, LongSumm 2020. In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 270\u2013276. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.sdp-1.30, https:\/\/aclanthology.org\/2020.sdp-1.30","DOI":"10.18653\/v1\/2020.sdp-1.30"},{"key":"22_CR32","unstructured":"Nakov, P., Schwartz, A.S., Hearst, M.A.: Citances: citation sentences for semantic analysis of bioscience text (2004)"},{"key":"22_CR33","doi-asserted-by":"publisher","unstructured":"Nallapati, R., Zhou, B., dos Santos, C.N., G\u00fcl\u00e7ehre, \u00c7., Xiang, B.: Abstractive text summarization using sequence-to-sequence RNNs and beyond. In: Goldberg, Y., Riezler, S. (eds.) Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, Berlin, Germany, 11\u201312 August 2016, pp. 280\u2013290. ACL (2016). https:\/\/doi.org\/10.18653\/v1\/k16-1028","DOI":"10.18653\/v1\/k16-1028"},{"key":"22_CR34","doi-asserted-by":"publisher","unstructured":"Parveen, D., Ramsl, H.M., Strube, M.: Topical coherence for graph-based extractive summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1949\u20131954. Association for Computational Linguistics, Lisbon (2015). https:\/\/doi.org\/10.18653\/v1\/D15-1226, https:\/\/aclanthology.org\/D15-1226","DOI":"10.18653\/v1\/D15-1226"},{"key":"22_CR35","unstructured":"Qazvinian, V., et al.: Generating extractive summaries of scientific paradigms. CoRR abs\/1402.0556 (2014). http:\/\/arxiv.org\/abs\/1402.0556"},{"key":"22_CR36","doi-asserted-by":"publisher","unstructured":"Reddy, S., Saini, N., Saha, S., Bhattacharyya, P.: IIITBH-IITP@CL-SciSumm20, CL-LaySumm20, LongSumm20. In: Proceedings of the First Workshop on Scholarly Document Processing, pp. 242\u2013250. Association for Computational Linguistics, Online (2020). https:\/\/doi.org\/10.18653\/v1\/2020.sdp-1.27, https:\/\/aclanthology.org\/2020.sdp-1.27","DOI":"10.18653\/v1\/2020.sdp-1.27"},{"key":"22_CR37","doi-asserted-by":"publisher","unstructured":"Shailabh, S., Chaurasia, S., Modi, A.: KnowGraph@IITK at SemEval-2021 task 11: building knowledge graph for NLP research. In: Palmer, A., Schneider, N., Schluter, N., Emerson, G., Herbelot, A., Zhu, X. (eds.) Proceedings of the 15th International Workshop on Semantic Evaluation, SemEval@ACL\/IJCNLP 2021, Virtual Event\/Bangkok, Thailand, 5\u20136 August 2021, pp. 467\u2013477. Association for Computational Linguistics (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.57","DOI":"10.18653\/v1\/2021.semeval-1.57"},{"key":"22_CR38","unstructured":"Sotudeh, S., Goharian, N.: GUIR @ MuP 2022: towards generating topic-aware multi-perspective summaries for scientific documents. In: Proceedings of the Third Workshop on Scholarly Document Processing, pp. 273\u2013278. Association for Computational Linguistics, Gyeongju (2022). https:\/\/aclanthology.org\/2022.sdp-1.34"},{"key":"22_CR39","unstructured":"Subramanian, S., Li, R., Pilault, J., Pal, C.J.: On extractive and abstractive neural document summarization with transformer language models. CoRR abs\/1909.03186 (2019). http:\/\/arxiv.org\/abs\/1909.03186"},{"key":"22_CR40","unstructured":"Urlana, A., Surange, N., Shrivastava, M.: LTRC @MuP 2022: multi-perspective scientific document summarization using pre-trained generation models. In: Proceedings of the Third Workshop on Scholarly Document Processing, pp. 279\u2013284. Association for Computational Linguistics, Gyeongju (2022). https:\/\/aclanthology.org\/2022.sdp-1.35"},{"key":"22_CR41","unstructured":"Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 4\u20139 December 2017, Long Beach, CA, USA, pp. 5998\u20136008 (2017). https:\/\/proceedings.neurips.cc\/paper\/2017\/hash\/3f5ee243547dee91fbd053c1c4a845aa-Abstract.html"},{"key":"22_CR42","unstructured":"Vaswani, A., et al.: Attention is all you need. CoRR abs\/1706.03762 (2017). http:\/\/arxiv.org\/abs\/1706.03762"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Yasunaga, M., et al.: ScisummNet: a large annotated corpus and content-impact models for scientific paper summarization with citation networks. In: Proceedings of AAAI 2019 (2019)","DOI":"10.1609\/aaai.v33i01.33017386"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Zechner, K.: Fast generation of abstracts from general domain text corpora by extracting relevant sentences. In: 16th International Conference on Computational Linguistics, Proceedings of the Conference, COLING 1996, Center for Sprogteknologi, Copenhagen, Denmark, 5\u20139 August 1996, pp. 986\u2013989 (1996). https:\/\/aclanthology.org\/C96-2166\/","DOI":"10.3115\/993268.993338"},{"key":"22_CR45","doi-asserted-by":"publisher","unstructured":"Zhang, G., Su, Y., He, C., Lin, L., Sun, C., Shan, L.: ITNLP at SemEval-2021 task 11: boosting BERT with sampling and adversarial training for knowledge extraction. In: Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pp. 485\u2013489. Association for Computational Linguistics, Online (2021). https:\/\/doi.org\/10.18653\/v1\/2021.semeval-1.59, https:\/\/aclanthology.org\/2021.semeval-1.59","DOI":"10.18653\/v1\/2021.semeval-1.59"}],"container-title":["Lecture Notes in Computer Science","Leveraging Generative Intelligence in Digital Libraries: Towards Human-Machine Collaboration"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8088-8_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,29]],"date-time":"2023-11-29T05:04:47Z","timestamp":1701234287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8088-8_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819980871","9789819980888"],"references-count":45,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8088-8_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"30 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICADL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Asian Digital Libraries","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taipei","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiwan","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":"4 December 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"7 December 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icadl2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/icadl.net\/icadl2023\/index.html","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":"85","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":"15","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":"17","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":"18% - 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":"1,92","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":"2 practice papers and 12 poster papers","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)"}}]}}