{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T15:46:36Z","timestamp":1743003996542,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":38,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819783663"},{"type":"electronic","value":"9789819783670"}],"license":[{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,11,29]],"date-time":"2024-11-29T00:00:00Z","timestamp":1732838400000},"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\/978-981-97-8367-0_26","type":"book-chapter","created":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T11:54:58Z","timestamp":1732794898000},"page":"434-450","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Going Beyond Passages: Readability Assessment for\u00a0Book-Level Long Texts"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8550-8165","authenticated-orcid":false,"given":"Wenbiao","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-9165-3025","authenticated-orcid":false,"given":"Rui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4464-0777","authenticated-orcid":false,"given":"Tianyi","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9560-7512","authenticated-orcid":false,"given":"Yunfang","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,11,29]]},"reference":[{"key":"26_CR1","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1162\/tacl_a_00278","volume":"7","author":"IM Azpiazu","year":"2019","unstructured":"Azpiazu, I.M., Pera, M.S.: Multiattentive recurrent neural network architecture for multilingual readability assessment. Trans. Assoc. Comput. Linguist. 7, 421\u2013436 (2019)","journal-title":"Trans. Assoc. Comput. Linguist."},{"key":"26_CR2","unstructured":"Beltagy, I., Peters, M.E., Cohan, A.: Longformer: the long-document transformer. arXiv preprint arXiv:2004.05150 (2020)"},{"key":"26_CR3","unstructured":"Choromanski, K., et\u00a0al.: Rethinking attention with performers. arXiv preprint arXiv:2009.14794 (2020)"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Cirstea, R.G., Guo, C., Yang, B., Kieu, T., Dong, X., Pan, S.: Triformer: triangular, variable-specific attentions for long sequence multivariate time series forecasting\u2013full version. arXiv preprint arXiv:2204.13767 (2022)","DOI":"10.24963\/ijcai.2022\/277"},{"key":"26_CR5","unstructured":"Dale, E., Chall, J.S.: A formula for predicting readability: instructions. Educ. Res. Bullet. 37\u201354 (1948)"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Deutsch, T., Jasbi, M., Shieber, S.: Linguistic features for readability assessment. arXiv preprint arXiv:2006.00377 (2020)","DOI":"10.18653\/v1\/2020.bea-1.1"},{"key":"26_CR7","unstructured":"Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"issue":"3","key":"26_CR8","doi-asserted-by":"publisher","first-page":"221","DOI":"10.1037\/h0057532","volume":"32","author":"R Flesch","year":"1948","unstructured":"Flesch, R.: A new readability yardstick. J. Appl. Psychol. 32(3), 221 (1948)","journal-title":"J. Appl. Psychol."},{"key":"26_CR9","unstructured":"Humeau, S., Shuster, K., Lachaux, M.A., Weston, J.: Poly-encoders: transformer architectures and pre-training strategies for fast and accurate multi-sentence scoring. arXiv preprint arXiv:1905.01969 (2019)"},{"key":"26_CR10","unstructured":"Imperial, J.M.: Bert embeddings for automatic readability assessment. arXiv preprint arXiv:2106.07935 (2021)"},{"key":"26_CR11","unstructured":"Jiang, Z., Gu, Q., Yin, Y., Chen, D.: Enriching word embeddings with domain knowledge for readability assessment. In: Proceedings of the 27th International Conference on Computational Linguistics, pp. 366\u2013378 (2018)"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Johnson, R., Zhang, T.: Deep pyramid convolutional neural networks for text categorization. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 562\u2013570 (2017)","DOI":"10.18653\/v1\/P17-1052"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Conference on Empirical Methods in Natural Language Processing (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"26_CR14","unstructured":"Kitaev, N., Kaiser, \u0141., Levskaya, A.: Reformer: the efficient transformer. arXiv preprint arXiv:2001.04451 (2020)"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Kong, W., et al.: Multi-aspect dense retrieval. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 3178\u20133186 (2022)","DOI":"10.1145\/3534678.3539137"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Lee, B.W., Jang, Y.S., Lee, J.H.J.: Pushing on text readability assessment: a transformer meets handcrafted linguistic features. arXiv preprint arXiv:2109.12258 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.834"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Lee, J., Vajjala, S.: A neural pairwise ranking model for readability assessment. arXiv preprint arXiv:2203.07450 (2022)","DOI":"10.18653\/v1\/2022.findings-acl.300"},{"key":"26_CR18","doi-asserted-by":"crossref","unstructured":"Lee-Thorp, J., Ainslie, J., Eckstein, I., Ontanon, S.: Fnet: mixing tokens with Fourier transforms. arXiv preprint arXiv:2105.03824 (2021)","DOI":"10.18653\/v1\/2022.naacl-main.319"},{"key":"26_CR19","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, Z., Wu, Y.: A unified neural network model for readability assessment with feature projection and length-balanced loss. arXiv preprint arXiv:2210.10305 (2022)","DOI":"10.18653\/v1\/2022.emnlp-main.504"},{"key":"26_CR20","unstructured":"Liu, S., et al.: Pyraformer: low-complexity pyramidal attention for long-range time series modeling and forecasting. In: International Conference on Learning Representations (2021)"},{"key":"26_CR21","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1007\/978-3-030-38189-9_40","volume-title":"Chinese Lexical Semantics","author":"D Lu","year":"2020","unstructured":"Lu, D., Qiu, X., Cai, Y.: Sentence-level readability assessment for L2 Chinese learning. In: Hong, J.-F., Zhang, Y., Liu, P. (eds.) CLSW 2019. LNCS (LNAI), vol. 11831, pp. 381\u2013392. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-38189-9_40"},{"key":"26_CR22","unstructured":"Ma, Y., Fosler-Lussier, E., Lofthus, R.: Ranking-based readability assessment for early primary children\u2019s literature. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 548\u2013552 (2012)"},{"issue":"8","key":"26_CR23","first-page":"639","volume":"12","author":"GH Mc Laughlin","year":"1969","unstructured":"Mc Laughlin, G.H.: Smog grading-a new readability formula. J. Read. 12(8), 639\u2013646 (1969)","journal-title":"J. Read."},{"key":"26_CR24","unstructured":"Paszke, A., et\u00a0al.: Pytorch: an imperative style, high-performance deep learning library. Adv. Neural Inf. Process. Syst. 32 (2019)"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Pera, M.S., Ng, Y.K.: Automating readers\u2019 advisory to make book recommendations for k-12 readers. In: Proceedings of the 8th ACM Conference on Recommender Systems, pp. 9\u201316 (2014)","DOI":"10.1145\/2645710.2645721"},{"key":"26_CR26","doi-asserted-by":"crossref","unstructured":"Perni, S., et al.: Assessment of use, specificity, and readability of written clinical informed consent forms for patients with cancer undergoing radiotherapy. JAMA Oncol. 5(8), e190260 (2019)","DOI":"10.1001\/jamaoncol.2019.0260"},{"issue":"1","key":"26_CR27","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.csl.2008.04.003","volume":"23","author":"SE Petersen","year":"2009","unstructured":"Petersen, S.E., Ostendorf, M.: A machine learning approach to reading level assessment. Comput. Speech Lang. 23(1), 89\u2013106 (2009)","journal-title":"Comput. Speech Lang."},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Qiu, X., Chen, Y., Chen, H., Nie, J.Y., Shen, Y., Lu, D.: Learning syntactic dense embedding with correlation graph for automatic readability assessment. arXiv preprint arXiv:2107.04268 (2021)","DOI":"10.18653\/v1\/2021.acl-long.235"},{"key":"26_CR29","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"771","DOI":"10.1007\/978-3-319-73618-1_67","volume-title":"Natural Language Processing and Chinese Computing","author":"X Qiu","year":"2018","unstructured":"Qiu, X., Deng, K., Qiu, L., Wang, X.: Exploring the impact of linguistic features for chinese readability assessment. In: Huang, X., Jiang, J., Zhao, D., Feng, Y., Hong, Yu. (eds.) NLPCC 2017. LNCS (LNAI), vol. 10619, pp. 771\u2013783. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-73618-1_67"},{"key":"26_CR30","unstructured":"Reyes, L.L.A., Iba\u00f1ez, M.A., Sapinit, R., Hussien, M., Imperial, J.M.: A baseline readability model for Cebuano. arXiv preprint arXiv:2203.17225 (2022)"},{"issue":"11","key":"26_CR31","doi-asserted-by":"publisher","first-page":"1549","DOI":"10.1016\/j.acra.2019.11.020","volume":"27","author":"A Sare","year":"2020","unstructured":"Sare, A., Patel, A., Kothari, P., Kumar, A., Patel, N., Shukla, P.A.: Readability assessment of internet-based patient education materials related to treatment options for benign prostatic hyperplasia. Acad. Radiol. 27(11), 1549\u20131554 (2020)","journal-title":"Acad. Radiol."},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Schwarm, S.E., Ostendorf, M.: Reading level assessment using support vector machines and statistical language models. In: Proceedings of the 43rd annual meeting of the Association for Computational Linguistics (ACL 2005), pp. 523\u2013530 (2005)","DOI":"10.3115\/1219840.1219905"},{"issue":"2","key":"26_CR33","doi-asserted-by":"publisher","first-page":"i","DOI":"10.1002\/j.2333-8504.2010.tb02235.x","volume":"2010","author":"KM Sheehan","year":"2010","unstructured":"Sheehan, K.M., Kostin, I., Futagi, Y., Flor, M.: Generating automated text complexity classifications that are aligned with targeted text complexity standards. ETS Research Report Series 2010(2), i\u201344 (2010)","journal-title":"ETS Research Report Series"},{"key":"26_CR34","unstructured":"Tan, C.H., Chen, Q., Wang, W., Zhang, Q., Zheng, S., Ling, Z.H.: Ponet: pooling network for efficient token mixing in long sequences. arXiv preprint arXiv:2110.02442 (2021)"},{"key":"26_CR35","unstructured":"Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)"},{"key":"26_CR36","unstructured":"Wang, S., Li, B.Z., Khabsa, M., Fang, H., Ma, H.: Linformer: self-attention with linear complexity. arXiv preprint arXiv:2006.04768 (2020)"},{"key":"26_CR37","doi-asserted-by":"crossref","unstructured":"Xu, Z., Zhao, M., Liu, L., Xiao, L., Zhang, X., Zhang, B.: Mixture of virtual-kernel experts for multi-objective user profile modeling. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 4257\u20134267 (2022)","DOI":"10.1145\/3534678.3539062"},{"key":"26_CR38","unstructured":"Zaheer, M., et al.: Big bird: transformers for longer sequences. Adv. Neural. Inf. Process. Syst. 33, 17283\u201317297 (2020)"}],"container-title":["Lecture Notes in Computer Science","Chinese Computational Linguistics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-97-8367-0_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,28]],"date-time":"2024-11-28T12:08:20Z","timestamp":1732795700000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-97-8367-0_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,29]]},"ISBN":["9789819783663","9789819783670"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-981-97-8367-0_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024,11,29]]},"assertion":[{"value":"29 November 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CCL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China National Conference on Chinese Computational Linguistics","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Taiyuan","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25 July 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 July 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cncl2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/cips-cl.org\/static\/CCL2024\/en\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}