{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T02:47:33Z","timestamp":1743130053349,"version":"3.40.3"},"publisher-location":"Cham","reference-count":37,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031208645"},{"type":"electronic","value":"9783031208652"}],"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\/978-3-031-20865-2_20","type":"book-chapter","created":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T23:29:12Z","timestamp":1667518152000},"page":"268-282","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving Abstractive Multi-document Summarization with\u00a0Predicate-Argument Structure Extraction"],"prefix":"10.1007","author":[{"given":"Huangfei","family":"Cheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiawei","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tiantian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,4]]},"reference":[{"key":"20_CR1","doi-asserted-by":"crossref","unstructured":"Aksoy, C., Bugdayci, A., Gur, T., Uysal, I., Can, F.: Semantic argument frequency-based multi-document summarization. In: 2009 24th International Symposium on Computer and Information Sciences, pp. 460\u2013464. IEEE (2009)","DOI":"10.1109\/ISCIS.2009.5291878"},{"key":"20_CR2","doi-asserted-by":"crossref","unstructured":"Bae, S., Kim, T., Kim, J., Lee, S.G.: Summary level training of sentence rewriting for abstractive summarization. In: Proceedings of the 2nd Workshop on New Frontiers in Summarization, pp. 10\u201320 (2019)","DOI":"10.18653\/v1\/D19-5402"},{"key":"20_CR3","doi-asserted-by":"crossref","unstructured":"Barzilay, R., McKeown, K., Elhadad, M.: Information fusion in the context of multi-document summarization. In: Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics, pp. 550\u2013557 (1999)","DOI":"10.3115\/1034678.1034760"},{"key":"20_CR4","unstructured":"Bastianelli, E., Castellucci, G., Croce, D., Basili, R.: Textual inference and meaning representation in human robot interaction. In: Proceedings of the Joint Symposium on Semantic Processing. Textual Inference and Structures in Corpora, pp. 65\u201369 (2013)"},{"key":"20_CR5","unstructured":"Bonial, C., Hwang, J., Bonn, J., Conger, K., Babko-Malaya, O., Palmer, M.: English propbank annotation guidelines. Center for Computational Language and Education Research, Institute of Cognitive Science, University of Colorado at Boulder, p. 48 (2012)"},{"key":"20_CR6","doi-asserted-by":"crossref","unstructured":"Carbonell, J., Goldstein, J.: The use of MMR, diversity-based reranking for reordering documents and producing summaries. In: Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 335\u2013336 (1998)","DOI":"10.1145\/290941.291025"},{"key":"20_CR7","doi-asserted-by":"crossref","unstructured":"Carreras, X., M\u00e0rquez, L.: Introduction to the CoNLL-2005 shared task: semantic role labeling. In: Proceedings of the Ninth Conference on Computational Natural Language Learning (CoNLL-2005), pp. 152\u2013164 (2005)","DOI":"10.3115\/1706543.1706571"},{"key":"20_CR8","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1613\/jair.1523","volume":"22","author":"G Erkan","year":"2004","unstructured":"Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457\u2013479 (2004)","journal-title":"J. Artif. Intell. Res."},{"key":"20_CR9","doi-asserted-by":"crossref","unstructured":"Fabbri, A.R., Li, I., She, T., Li, S., Radev, D.: Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1074\u20131084 (2019)","DOI":"10.18653\/v1\/P19-1102"},{"key":"20_CR10","unstructured":"Ganesan, K., Zhai, C.X., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: 23rd International Conference on Computational Linguistics, COLING 2010 (2010)"},{"key":"20_CR11","doi-asserted-by":"crossref","unstructured":"Gerani, S., Mehdad, Y., Carenini, G., Ng, R., Nejat, B.: Abstractive summarization of product reviews using discourse structure. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1602\u20131613 (2014)","DOI":"10.3115\/v1\/D14-1168"},{"key":"20_CR12","unstructured":"Ghalandari, D.G., Hokamp, C., Glover, J., Ifrim, G., et al.: A large-scale multi-document summarization dataset from the Wikipedia current events portal. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 1302\u20131308 (2020)"},{"key":"20_CR13","doi-asserted-by":"crossref","unstructured":"He, L., Lee, K., Lewis, M., Zettlemoyer, L.: Deep semantic role labeling: what works and what\u2019s next. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 473\u2013483 (2017)","DOI":"10.18653\/v1\/P17-1044"},{"key":"20_CR14","unstructured":"Kenton, J.D.M.W.C., Toutanova, L.K.: BERT: pre-training of deep bidirectional transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171\u20134186 (2019)"},{"key":"20_CR15","doi-asserted-by":"publisher","first-page":"737","DOI":"10.1016\/j.asoc.2015.01.070","volume":"30","author":"A Khan","year":"2015","unstructured":"Khan, A., Salim, N., Kumar, Y.J.: A framework for multi-document abstractive summarization based on semantic role labelling. Appl. Soft Comput. 30, 737\u2013747 (2015)","journal-title":"Appl. Soft Comput."},{"key":"20_CR16","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)"},{"key":"20_CR17","doi-asserted-by":"crossref","unstructured":"Lebanoff, L., et al.: Scoring sentence singletons and pairs for abstractive summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 2175\u20132189 (2019)","DOI":"10.18653\/v1\/P19-1209"},{"key":"20_CR18","doi-asserted-by":"crossref","unstructured":"Lebanoff, L., Song, K., Liu, F.: Adapting the neural encoder-decoder framework from single to multi-document summarization. In: EMNLP (2018)","DOI":"10.18653\/v1\/D18-1446"},{"key":"20_CR19","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"20_CR20","doi-asserted-by":"crossref","unstructured":"Li, W., Xiao, X., Liu, J., Wu, H., Wang, H., Du, J.: Leveraging graph to improve abstractive multi-document summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6232\u20136243 (2020)","DOI":"10.18653\/v1\/2020.acl-main.555"},{"key":"20_CR21","unstructured":"Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381 (2004)"},{"key":"20_CR22","unstructured":"Liu, P.J., et al.: Generating Wikipedia by summarizing long sequences. In: International Conference on Learning Representations (2018)"},{"key":"20_CR23","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lapata, M.: Hierarchical transformers for multi-document summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5070\u20135081 (2019)","DOI":"10.18653\/v1\/P19-1500"},{"key":"20_CR24","doi-asserted-by":"crossref","unstructured":"Mendes, A., Narayan, S., Miranda, S., Marinho, Z., Martins, A.F., Cohen, S.B.: Jointly extracting and compressing documents with summary state representations. 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), pp. 3955\u20133966 (2019)","DOI":"10.18653\/v1\/N19-1397"},{"key":"20_CR25","doi-asserted-by":"crossref","unstructured":"Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, pp. 404\u2013411 (2004)","DOI":"10.3115\/1220575.1220627"},{"key":"20_CR26","unstructured":"Over, P., Yen, J.: An introduction to DUC-2004. National Institute of Standards and Technology (2004)"},{"key":"20_CR27","doi-asserted-by":"crossref","unstructured":"Qi, W., et al.: ProphetNet: predicting future n-gram for sequence-to-sequencepre-training. In: Findings of the Association for Computational Linguistics: EMNLP 2020, pp. 2401\u20132410 (2020)","DOI":"10.18653\/v1\/2020.findings-emnlp.217"},{"issue":"6","key":"20_CR28","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1016\/j.ipm.2003.10.006","volume":"40","author":"DR Radev","year":"2004","unstructured":"Radev, D.R., Jing, H., Sty\u015b, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Process. Manag. 40(6), 919\u2013938 (2004)","journal-title":"Inf. Process. Manag."},{"key":"20_CR29","first-page":"1","volume":"21","author":"C Raffel","year":"2020","unstructured":"Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1\u201367 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"20_CR30","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: summarization with pointer-generator networks. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1073\u20131083 (2017)","DOI":"10.18653\/v1\/P17-1099"},{"key":"20_CR31","unstructured":"Shen, D., Lapata, M.: Using semantic roles to improve question answering. In: Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), pp. 12\u201321 (2007)"},{"key":"20_CR32","unstructured":"Shi, P., Lin, J.: Simple BERT models for relation extraction and semantic role labeling. arXiv preprint arXiv:1904.05255 (2019)"},{"key":"20_CR33","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"20_CR34","doi-asserted-by":"crossref","unstructured":"Wu, D., Fung, P.: Semantic roles for SMT: a hybrid two-pass model. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers, pp. 13\u201316 (2009)","DOI":"10.3115\/1620853.1620858"},{"key":"20_CR35","doi-asserted-by":"crossref","unstructured":"Zhang, H., Cai, J., Xu, J., Wang, J.: Pretraining-based natural language generation for text summarization. In: Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL), pp. 789\u2013797 (2019)","DOI":"10.18653\/v1\/K19-1074"},{"key":"20_CR36","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":"20_CR37","doi-asserted-by":"crossref","unstructured":"Zhong, M., Liu, P., Chen, Y., Wang, D., Qiu, X., Huang, X.: Extractive summarization as text matching. In: ACL (2020)","DOI":"10.18653\/v1\/2020.acl-main.552"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2022: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-20865-2_20","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,7]],"date-time":"2024-10-07T06:20:01Z","timestamp":1728282001000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-20865-2_20"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031208645","9783031208652"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-20865-2_20","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"4 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shangai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/pricai.org\/2022\/","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":"432","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":"91","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":"39","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":"21% - 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":"7-8","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":"n\/a","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)"}}]}}