{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T07:02:20Z","timestamp":1780729340825,"version":"3.54.1"},"publisher-location":"Singapore","reference-count":21,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819214617","type":"print"},{"value":"9789819214624","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-981-92-1462-4_1","type":"book-chapter","created":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:48:20Z","timestamp":1780728500000},"page":"3-15","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AVER: Adversarial Variational Enhanced Representation Architecture for\u00a0Abstractive Multi-document Summarization"],"prefix":"10.1007","author":[{"given":"Chaojie","family":"Sun","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinxin","family":"Guan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenyu","family":"Hou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tiantian","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,7]]},"reference":[{"key":"1_CR1","doi-asserted-by":"crossref","unstructured":"Puduppully, R., Jain, P., Chen, N.F., Steedman, M.: Multi-document summarization with centroid-based pretraining, (2022). arXiv:2208.01006 arXiv preprint","DOI":"10.18653\/v1\/2023.acl-short.13"},{"key":"1_CR2","doi-asserted-by":"crossref","unstructured":"Xiao, W., Beltagy, I., Carenini, G., Cohan, A.: Primera: pyramid-based masked sentence pre-training for multi-document summarization. In: Proceedings of the 60th annual meeting of the association for computational linguistics (Volume 1: Long Papers), pp. 5245\u20135263 (2022)","DOI":"10.18653\/v1\/2022.acl-long.360"},{"key":"1_CR3","doi-asserted-by":"crossref","unstructured":"Mao, Y., Qu, Y., Xie, Y., Ren, X., Han, J.: Multi-document summarization with maximal marginal relevance-guided reinforcement learning (2020). arXiv:2010.00117 arXiv preprint","DOI":"10.18653\/v1\/2020.emnlp-main.136"},{"key":"1_CR4","doi-asserted-by":"crossref","unstructured":"See, A., Liu, P.J., Manning, C.D.: Get to the point: Summarization with pointer-generator networks, (2017). arXiv:1704.04368 arXiv preprint","DOI":"10.18653\/v1\/P17-1099"},{"key":"1_CR5","doi-asserted-by":"crossref","unstructured":"Sie, M., Beek, R., Bots, M., Brinkkemper, S., Gatt, A.: Summarizing long regulatory documents with a multi-step pipeline (2024). arXiv:2408.09777 arXiv preprint","DOI":"10.18653\/v1\/2024.nllp-1.2"},{"key":"1_CR6","doi-asserted-by":"crossref","unstructured":"Huang, K.H., et al.: Embrace divergence for richer insights: a multi-document summarization benchmark and a case study on summarizing diverse information from news articles (2023). arXiv:2309.09369 arXiv preprint","DOI":"10.18653\/v1\/2024.naacl-long.32"},{"key":"1_CR7","doi-asserted-by":"crossref","unstructured":"Cao, B., Guan, X., Bao, S., Wu, J., Fan, J.: Optimizing representation for abstractive multi-document summarization based on adversarial learning strategy. IEEE Transactions on Cognitive and Developmental Systems (2025)","DOI":"10.1109\/TCDS.2025.3563357"},{"key":"1_CR8","doi-asserted-by":"crossref","unstructured":"Jin, H., Wang, T., Wan, X.: Multi-granularity interaction network for extractive and abstractive multi-document summarization. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pp. 6244\u20136254 (2020f","DOI":"10.18653\/v1\/2020.acl-main.556"},{"key":"1_CR9","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. arXiv preprint arXiv:2005.10043 (2020)","DOI":"10.18653\/v1\/2020.acl-main.555"},{"key":"1_CR10","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: 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. Bart (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"1_CR11","doi-asserted-by":"crossref","unstructured":"Guo, M., et al.: Longt5: Efficient text-to-text transformer for long sequences (2021). arXiv:2112.07916 arXiv preprint","DOI":"10.18653\/v1\/2022.findings-naacl.55"},{"key":"1_CR12","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: The long-document transformer (2020). arXiv:2004.05150 arXiv preprint"},{"key":"1_CR13","doi-asserted-by":"crossref","unstructured":"Pasunuru, R., Liu, M., Bansal, M., Ravi, S., Dreyer, M.: Efficiently summarizing text and graph encodings of multi-document clusters. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4768\u20134779 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.380"},{"key":"1_CR14","unstructured":"Kingma, D.P., Welling, M.: Auto-encoding variational bayes (2013)"},{"key":"1_CR15","doi-asserted-by":"crossref","unstructured":"Park, S., Lee, J.: Finetuning pretrained transformers into variational autoencoders, (2021). arXiv:2108.02446 arXiv preprint","DOI":"10.18653\/v1\/2021.insights-1.5"},{"key":"1_CR16","doi-asserted-by":"crossref","unstructured":"Li, C., et al.: Optimus: Organizing sentences via pre-trained modeling of a latent space. arXiv preprint arXiv:2004.04092 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.378"},{"key":"1_CR17","unstructured":"Ganin, Y., Lempitsky, V.: Unsupervised domain adaptation by backpropagation. In: International conference on machine learning, pp. 1180\u20131189 (2015) PMLR"},{"key":"1_CR18","doi-asserted-by":"crossref","unstructured":"Fabbri, A., Li, I., She, T., Li, S., Radev, D.: Multi-news: a large-scale multi-document summarization dataset and abstractive hierarchical model. In: Proceedings of ACL, pp. 1074\u20131084 (2019). https:\/\/arxiv.org\/abs\/1906.01749","DOI":"10.18653\/v1\/P19-1102"},{"key":"1_CR19","unstructured":"Dang, H.T.: Overview of duc 2004. In: Document Understanding Conference (2004). https:\/\/duc.nist.gov\/pubs\/2004papers\/overview.pdf"},{"key":"1_CR20","doi-asserted-by":"crossref","unstructured":"bibitemch1multispsxscience Lu, Y., Dong, Y., Charlin, L.: Multi-xscience: a large-scale dataset for extreme multi-document summarization of scientific articles. In: Proceedings of EMNLP, pp. 8068\u20138074 (2020). https:\/\/arxiv.org\/abs\/2004.09001","DOI":"10.18653\/v1\/2020.emnlp-main.648"},{"key":"1_CR21","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 (2020) PMLR"}],"container-title":["Lecture Notes in Computer Science","Advances in Knowledge Discovery and Data Mining"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-92-1462-4_1","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T06:48:22Z","timestamp":1780728502000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-92-1462-4_1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9789819214617","9789819214624"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-981-92-1462-4_1","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"7 June 2026","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 Interest"}},{"value":"PAKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific-Asia Conference on Knowledge Discovery and Data Mining","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","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":"2026","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 June 2026","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 June 2026","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pakdd2026","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pakdd2026.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}