{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T22:40:09Z","timestamp":1751409609545,"version":"3.41.0"},"publisher-location":"Singapore","reference-count":37,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819666010","type":"print"},{"value":"9789819665990","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-6599-0_24","type":"book-chapter","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T22:15:41Z","timestamp":1751408141000},"page":"347-362","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Tailored Domain-Specific Summaries: A Two-Stage Method Combining Extractive and\u00a0Abstractive Summarization Models"],"prefix":"10.1007","author":[{"given":"Feng","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lingyi","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haizhou","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,7,2]]},"reference":[{"key":"24_CR1","doi-asserted-by":"crossref","unstructured":"Basu Roy\u00a0Chowdhury, S., Zhao, C., Chaturvedi, S.: Unsupervised extractive opinion summarization using sparse coding. In: ACL, pp. 1209\u20131225 (2022)","DOI":"10.18653\/v1\/2022.acl-long.86"},{"key":"24_CR2","doi-asserted-by":"crossref","unstructured":"Bleiweiss, A.: Two-step text summarization for long-form biographical narrative genre. In: Strube, M., Braud, C., Hardmeier, C., Li, J.J., Loaiciga, S., Zeldes, A. (eds.) CODI 2023, pp. 145\u2013155 (2023)","DOI":"10.18653\/v1\/2023.codi-1.20"},{"issue":"1\u20137","key":"24_CR3","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/S0169-7552(98)00110-X","volume":"30","author":"S Brin","year":"1998","unstructured":"Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1\u20137), 107\u2013117 (1998)","journal-title":"Comput. Netw. ISDN Syst."},{"key":"24_CR4","doi-asserted-by":"crossref","unstructured":"Cheang, C., et al.: Can LMs generalize to future data? An empirical analysis on text summarization. In: EMNLP, pp. 16205\u201316217 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.1007"},{"key":"24_CR5","doi-asserted-by":"crossref","unstructured":"Dixit, T., Wang, F., Chen, M.: Improving factuality of abstractive summarization without sacrificing summary quality. In: ACL, pp. 902\u2013913 (2023)","DOI":"10.18653\/v1\/2023.acl-short.78"},{"key":"24_CR6","doi-asserted-by":"crossref","unstructured":"Egonmwan, E., Chali, Y.: Transformer-based model for single documents neural summarization. In: Proceedings of the 3rd Workshop on Neural Generation and Translation, pp. 70\u201379 (2019)","DOI":"10.18653\/v1\/D19-5607"},{"key":"24_CR7","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":"24_CR8","doi-asserted-by":"crossref","unstructured":"Gehrmann, S., Deng, Y., Rush, A.: Bottom-up abstractive summarization. In: EMNLP, pp. 4098\u20134109 (2018)","DOI":"10.18653\/v1\/D18-1443"},{"key":"24_CR9","doi-asserted-by":"crossref","unstructured":"Guan, S., Padmakumar, V.: Extract, select and rewrite: a modular sentence summarization method. In: Proceedings of the 4th New Frontiers in Summarization Workshop, pp. 41\u201348 (2023)","DOI":"10.18653\/v1\/2023.newsum-1.4"},{"key":"24_CR10","doi-asserted-by":"crossref","unstructured":"Kornilova, A., Eidelman, V.: BillSum: a corpus for automatic summarization of US legislation. In: Proceedings of the 2nd Workshop on New Frontiers in Summarization, pp. 48\u201356 (2019)","DOI":"10.18653\/v1\/D19-5406"},{"issue":"1","key":"24_CR11","doi-asserted-by":"publisher","first-page":"164","DOI":"10.1177\/0165551521990616","volume":"49","author":"S Lamsiyah","year":"2023","unstructured":"Lamsiyah, S., Mahdaouy, A.E., Ouatik, S., Espinasse, B.: Unsupervised extractive multi-document summarization method based on transfer learning from BERT multi-task fine-tuning. J. Inf. Sci. 49(1), 164\u2013182 (2023)","journal-title":"J. Inf. Sci."},{"key":"24_CR12","doi-asserted-by":"crossref","unstructured":"Lewis, M., et al.: BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL, pp. 7871\u20137880 (2020)","DOI":"10.18653\/v1\/2020.acl-main.703"},{"key":"24_CR13","unstructured":"Liang, X., et al.: An efficient coarse-to-fine facet-aware unsupervised summarization framework based on semantic blocks. In: COLING, pp. 6415\u20136425 (2022)"},{"key":"24_CR14","doi-asserted-by":"crossref","unstructured":"Lim, J., Song, H.J.: Improving multi-stage long document summarization with enhanced coarse summarizer. In: Proceedings of the 4th New Frontiers in Summarization Workshop, pp. 135\u2013144 (2023)","DOI":"10.18653\/v1\/2023.newsum-1.13"},{"key":"24_CR15","unstructured":"Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out, pp. 74\u201381. Association for Computational Linguistics, Barcelona, Spain, July 2004. https:\/\/aclanthology.org\/W04-1013"},{"key":"24_CR16","unstructured":"Liu, Y.: Fine-tune BERT for extractive summarization. arXiv preprint arXiv:1903.10318 (2019)"},{"key":"24_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Y., Lapata, M.: Text summarization with pretrained encoders. In: Inui, K., Jiang, J., Ng, V., Wan, X. (eds.) EMNLP-IJCNLP, pp. 3730\u20133740 (2019)","DOI":"10.18653\/v1\/D19-1387"},{"key":"24_CR18","doi-asserted-by":"crossref","unstructured":"Mao, Q., et al.: Bipartite graph pre-training for unsupervised extractive summarization with graph convolutional auto-encoders. In: Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 4929\u20134941 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.328"},{"key":"24_CR19","unstructured":"Mihalcea, R., Tarau, P.: Textrank: bringing order into text. In: EMNLP, pp. 404\u2013411 (2004)"},{"key":"24_CR20","doi-asserted-by":"crossref","unstructured":"Pilault, J., Li, R., Subramanian, S., Pal, C.: On extractive and abstractive neural document summarization with transformer language models. In: EMNLP, pp. 9308\u20139319 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.748"},{"issue":"1","key":"24_CR21","first-page":"5485","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), 5485\u20135551 (2020)","journal-title":"J. Mach. Learn. Res."},{"key":"24_CR22","doi-asserted-by":"crossref","unstructured":"Roit, P., et al.: Factually consistent summarization via reinforcement learning with textual entailment feedback. In: ACL, pp. 6252\u20136272 (2023)","DOI":"10.18653\/v1\/2023.acl-long.344"},{"key":"24_CR23","doi-asserted-by":"crossref","unstructured":"Ruan, Q., Ostendorff, M., Rehm, G.: HiStruct+: improving extractive text summarization with hierarchical structure information. In: Findings of the Association for Computational Linguistics: ACL 2022, pp. 1292\u20131308 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.102"},{"key":"24_CR24","unstructured":"Su, J.: T5 pegasus - zhuiyiai. Technical report (2021). https:\/\/github.com\/ZhuiyiTechnology\/t5-pegasus"},{"key":"24_CR25","doi-asserted-by":"crossref","unstructured":"Sul, J., Choi, Y.S.: Balancing lexical and semantic quality in abstractive summarization. In: ACL, pp. 637\u2013647 (2023)","DOI":"10.18653\/v1\/2023.acl-short.56"},{"key":"24_CR26","doi-asserted-by":"crossref","unstructured":"Wan, D., Liu, M., McKeown, K., Dreyer, M., Bansal, M.: Faithfulness-aware decoding strategies for abstractive summarization. In: EACL, pp. 2864\u20132880 (2023)","DOI":"10.18653\/v1\/2023.eacl-main.210"},{"key":"24_CR27","doi-asserted-by":"crossref","unstructured":"Xue, L., et al.: mT5: a massively multilingual pre-trained text-to-text transformer. In: NAACL-HLT, pp. 483\u2013498 (2021)","DOI":"10.18653\/v1\/2021.naacl-main.41"},{"key":"24_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, X., Zhang, J.: Contrastive hierarchical discourse graph for scientific document summarization. In: CODI 2023, pp. 37\u201347 (2023)","DOI":"10.18653\/v1\/2023.codi-1.4"},{"key":"24_CR29","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, X., Zhang, J.: Extractive summarization via ChatGPT for faithful summary generation. arXiv preprint arXiv:2304.04193 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.214"},{"key":"24_CR30","doi-asserted-by":"crossref","unstructured":"Zhang, H., Liu, X., Zhang, J.: SummIt: iterative text summarization via ChatGPT. In: Bouamor, H., Pino, J., Bali, K. (eds.) Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 10644\u201310657 (2023)","DOI":"10.18653\/v1\/2023.findings-emnlp.714"},{"key":"24_CR31","doi-asserted-by":"crossref","unstructured":"Zhang, H., Yavuz, S., Kryscinski, W., Hashimoto, K., Zhou, Y.: Improving the faithfulness of abstractive summarization via entity coverage control. In: Findings of the Association for Computational Linguistics: NAACL 2022, pp. 528\u2013535 (2022)","DOI":"10.18653\/v1\/2022.findings-naacl.40"},{"key":"24_CR32","unstructured":"Zhang, J., Zhao, Y., Saleh, M., Liu, P.: Pegasus: pre-training with extracted gap-sentences for abstractive summarization. In: ICML, pp. 11328\u201311339 (2020)"},{"key":"24_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, N., Zhang, Y., Guo, W., Mitra, P., Zhang, R.: FaMeSumm: investigating and improving faithfulness of medical summarization. In: EMNLP, pp. 10915\u201310931 (2023)","DOI":"10.18653\/v1\/2023.emnlp-main.673"},{"key":"24_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, T., Ladhak, F., Durmus, E., Liang, P., McKeown, K., Hashimoto, T.B.: Benchmarking large language models for news summarization (2023)","DOI":"10.1162\/tacl_a_00632"},{"key":"24_CR35","unstructured":"Zhang, Y., et al.: Summ$$^n$$: a multi-stage summarization framework for long input dialogues and documents. In: ACL, pp. 1592\u20131604 (2022)"},{"key":"24_CR36","doi-asserted-by":"crossref","unstructured":"Zheng, H., Lapata, M.: Sentence centrality revisited for unsupervised summarization. In: ACL, pp. 6236\u20136247. Florence, Italy (2019)","DOI":"10.18653\/v1\/P19-1628"},{"key":"24_CR37","unstructured":"Zheng, L., et\u00a0al.: Judging LLM-as-a-judge with MT-bench and chatbot arena. IN: Advances in Neural Information Processing Systems, vol. 36 (2024)"}],"container-title":["Lecture Notes in Computer Science","Neural Information Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-6599-0_24","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T22:15:46Z","timestamp":1751408146000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-6599-0_24"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819666010","9789819665990"],"references-count":37,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-6599-0_24","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"2 July 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICONIP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Information Processing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Auckland","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"New Zealand","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":"2 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"31","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iconip2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/iconip2024.org","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}