{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,6]],"date-time":"2025-07-06T04:01:05Z","timestamp":1751774465673,"version":"3.41.0"},"reference-count":36,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2025,7,1]]},"DOI":"10.1587\/transinf.2024edp7109","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T22:12:56Z","timestamp":1736979176000},"page":"808-819","source":"Crossref","is-referenced-by-count":0,"title":["Title Information for Transformer-Based Japanese Document Emphasis"],"prefix":"10.1587","volume":"E108.D","author":[{"given":"Binggang","family":"ZHUO","sequence":"first","affiliation":[{"name":"Tottori University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ryota","family":"HONDA","sequence":"additional","affiliation":[{"name":"Ricoh IT Solutions Co.,Ltd."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Masaki","family":"MURATA","sequence":"additional","affiliation":[{"name":"Tottori University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] D. Greene and P. Cunningham, \u201cPractical solutions to the problem of diagonal dominance in kernel document clustering,\u201d Proc. 23rd International Conference on Machine learning (ICML\u201906), pp.377-384, ACM Press, 2006. 10.1145\/1143844.1143892","DOI":"10.1145\/1143844.1143892"},{"key":"2","doi-asserted-by":"publisher","unstructured":"[2] M. Murata and Y. Abe, \u201cUsing machine learning for automatic estimation of emphases in japanese documents,\u201d IEICE Transactions on Information and Systems, vol.E100-D, no.10, pp.2669-2672, 2017. 10.1587\/transinf.2016edl8247","DOI":"10.1587\/transinf.2016EDL8247"},{"key":"3","unstructured":"[3] J. Lafferty, A. McCallum, and F.C. Pereira, \u201cConditional random fields: Probabilistic models for segmenting and labeling sequence data,\u201d 2001."},{"key":"4","unstructured":"[4] Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, \u201cRoberta: A robustly optimized bert pretraining approach,\u201d arXiv preprint arXiv:1907.11692, 2019."},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, \u201cEnriching word vectors with subword information,\u201d Transactions of the association for computational linguistics, vol.5, pp.135-146, 2017. 10.1162\/tacl_a_00051","DOI":"10.1162\/tacl_a_00051"},{"key":"6","unstructured":"[6] J. Devlin, M.W. Chang, K. Lee, and K. Toutanova, \u201cBert: Pre-training of deep bidirectional transformers for language understanding,\u201d arXiv preprint arXiv:1810.04805, 2018."},{"key":"7","unstructured":"[7] T. Brown, B. Mann, N. Ryder, M. Subbiah, J.D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al., \u201cLanguage models are few-shot learners,\u201d Advances in neural information processing systems, vol.33, pp.1877-1901, 2020."},{"key":"8","unstructured":"[8] S. Bubeck, V. Chandrasekaran, R. Eldan, J. Gehrke, E. Horvitz, E. Kamar, P. Lee, Y.T. Lee, Y. Li, S. Lundberg, et al., \u201cSparks of artificial general intelligence: Early experiments with gpt-4,\u201d arXiv preprint arXiv:2303.12712, 2023."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] M. Allahyari, S. Pouriyeh, M. Assefi, S. Safaei, E.D. Trippe, J.B. Gutierrez, and K. Kochut, \u201cText summarization techniques: a brief survey,\u201d arXiv preprint arXiv:1707.02268, 2017.","DOI":"10.14569\/IJACSA.2017.081052"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] A. Choudhary, M. Alugubelly, and R. Bhargava, \u201cA comparative study on transformer-based news summarization,\u201d 2023 15th International Conference on Developments in eSystems Engineering (DeSE), pp.256-261, IEEE, 2023. 10.1109\/dese58274.2023.10099798","DOI":"10.1109\/DeSE58274.2023.10099798"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] H. Batra, A. Jain, G. Bisht, K. Srivastava, M. Bharadwaj, D. Bajaj, and U. Bharti, \u201cCovshorts: news summarization application based on deep nlp transformers for sars-cov-2,\u201d 2021 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), pp.1-6, IEEE, 2021. 10.1109\/icrito51393.2021.9596520","DOI":"10.1109\/ICRITO51393.2021.9596520"},{"key":"12","unstructured":"[12] Y. Liu, \u201cFine-tune bert for extractive summarization,\u201d arXiv preprint arXiv:1903.10318, 2019."},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] A. Deroy, K. Ghosh, and S. Ghosh, \u201cEnsemble methods for improving extractive summarization of legal case judgements,\u201d Artificial Intelligence and Law, vol.32, no.1, pp.231-289, 2024. 10.1007\/s10506-023-09349-8","DOI":"10.1007\/s10506-023-09349-8"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] H. Zhang, X. Liu, and J. Zhang, \u201cDiffusum: Generation enhanced extractive summarization with diffusion,\u201d arXiv preprint arXiv:2305.01735, 2023.","DOI":"10.18653\/v1\/2023.findings-acl.828"},{"key":"15","doi-asserted-by":"publisher","unstructured":"[15] S. Abdel-Salam and A. Rafea, \u201cPerformance study on extractive text summarization using bert models,\u201d Information, vol.13, no.2, p.67, 2022. 10.3390\/info13020067","DOI":"10.3390\/info13020067"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] M. Zhong, P. Liu, Y. Chen, D. Wang, X. Qiu, and X. Huang, \u201cExtractive summarization as text matching,\u201d arXiv preprint arXiv:2004.08795, 2020.","DOI":"10.18653\/v1\/2020.acl-main.552"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Q. Zhou, F. Wei, and M. Zhou, \u201cAt which level should we extract? an empirical analysis on extractive document summarization,\u201d Proceedings of the 28th International Conference on Computational Linguistics, pp.5617-5628, 2020. 10.18653\/v1\/2020.coling-main.492","DOI":"10.18653\/v1\/2020.coling-main.492"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] A. Shirani, F. Dernoncourt, N. Lipka, P. Asente, J. Echevarria, and T. Solorio, \u201cSemeval-2020 task 10: Emphasis selection for written text in visual media,\u201d arXiv preprint arXiv:2008.03274, 2020.","DOI":"10.18653\/v1\/2020.semeval-1.184"},{"key":"19","unstructured":"[19] T. Mikolov, K. Chen, G. Corrado, and J. Dean, \u201cEfficient estimation of word representations in vector space,\u201d arXiv preprint arXiv:1301.3781, 2013."},{"key":"20","doi-asserted-by":"crossref","unstructured":"[20] J. Pennington, R. Socher, and C.D. Manning, \u201cGlove: Global vectors for word representation,\u201d Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pp.1532-1543, 2014. 10.3115\/v1\/d14-1162","DOI":"10.3115\/v1\/D14-1162"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] M.E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, \u201cDeep contextualized word representations,\u201d Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pp.2227-2237, 2018. 10.18653\/v1\/n18-1202","DOI":"10.18653\/v1\/N18-1202"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural computation, vol.9, no.8, pp.1735-1780, 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"23","unstructured":"[23] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, \u201cEmpirical evaluation of gated recurrent neural networks on sequence modeling,\u201d arXiv preprint arXiv:1412.3555, 2014."},{"key":"24","unstructured":"[24] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d Advances in neural information processing systems, vol.30, 2017."},{"key":"25","doi-asserted-by":"publisher","unstructured":"[25] H. Wang, J. Li, H. Wu, E. Hovy, and Y. Sun, \u201cPre-trained language models and their applications,\u201d Engineering, vol.25, pp.51-65, 2022. 10.1016\/j.eng.2022.04.024","DOI":"10.1016\/j.eng.2022.04.024"},{"key":"26","unstructured":"[26] S. Narayan, N. Papasarantopoulos, S.B. Cohen, and M. Lapata, \u201cNeural extractive summarization with side information,\u201d arXiv preprint arXiv:1704.04530, 2017."},{"key":"27","unstructured":"[27] A. Abdul-Hamid and K. Darwish, \u201cSimplified feature set for arabic named entity recognition,\u201d Proceedings of the 2010 named entities workshop, pp.110-115, 2010."},{"key":"28","doi-asserted-by":"crossref","unstructured":"[28] G. Lample, M. Ballesteros, S. Subramanian, K. Kawakami, and C. Dyer, \u201cNeural architectures for named entity recognition,\u201d arXiv preprint arXiv:1603.01360, 2016.","DOI":"10.18653\/v1\/N16-1030"},{"key":"29","unstructured":"[29] Z. Huang, W. Xu, and K. Yu, \u201cBidirectional lstm-crf models for sequence tagging,\u201d arXiv preprint arXiv:1508.01991, 2015."},{"key":"30","unstructured":"[30] F. Souza, R. Nogueira, and R. Lotufo, \u201cPortuguese named entity recognition using bert-crf,\u201d arXiv preprint arXiv:1909.10649, 2019."},{"key":"31","doi-asserted-by":"crossref","unstructured":"[31] D. Jurkiewicz, \u0141. Borchmann, I. Kosmala, and F. Grali\u0144ski, \u201cApplicaai at semeval-2020 task 11: On roberta-crf, span cls and whether self-training helps them,\u201d arXiv preprint arXiv:2005.07934, 2020.","DOI":"10.18653\/v1\/2020.semeval-1.187"},{"key":"32","doi-asserted-by":"publisher","unstructured":"[32] R.C. Blair and J.J. Higgins, \u201cComparison of the power of the paired samples t test to that of wilcoxon\u2019s signed-ranks test under various population shapes.,\u201d Psychological Bulletin, vol.97, no.1, pp.119-128, 1985. 10.1037\/\/0033-2909.97.1.119","DOI":"10.1037\/\/0033-2909.97.1.119"},{"key":"33","doi-asserted-by":"crossref","unstructured":"[33] B. Efron, \u201cBootstrap methods: another look at the jackknife,\u201d Breakthroughs in statistics: Methodology and distribution, pp.569-593, Springer, 1992. 10.1007\/978-1-4612-4380-9_41","DOI":"10.1007\/978-1-4612-4380-9_41"},{"key":"34","unstructured":"[34] B. Guo, X. Zhang, Z. Wang, M. Jiang, J. Nie, Y. Ding, J. Yue, and Y. Wu, \u201cHow close is chatgpt to human experts? comparison corpus, evaluation, and detection,\u201d arXiv preprint arXiv:2301.07597, 2023."},{"key":"35","unstructured":"[35] T. Kuzman, I. Mozetic, and N. Ljube\u0161ic, \u201cChatgpt: Beginning of an end of manual linguistic data annotation? use case of automatic genre identification,\u201d ArXiv, abs\/2303.03953, 2023."},{"key":"36","unstructured":"[36] E. Perez, D. Kiela, and K. Cho, \u201cTrue few-shot learning with language models,\u201d Advances in neural information processing systems, vol.34, pp.11054-11070, 2021."}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/7\/E108.D_2024EDP7109\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,7,5]],"date-time":"2025-07-05T03:35:51Z","timestamp":1751686551000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E108.D\/7\/E108.D_2024EDP7109\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,1]]},"references-count":36,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2024edp7109","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2025,7,1]]},"article-number":"2024EDP7109"}}