{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:14:39Z","timestamp":1743009279838,"version":"3.40.3"},"publisher-location":"Cham","reference-count":35,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031355066"},{"type":"electronic","value":"9783031355073"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-3-031-35507-3_30","type":"book-chapter","created":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T21:04:13Z","timestamp":1685739853000},"page":"311-324","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Survey on Controllable Abstractive Text Summarization"],"prefix":"10.1007","author":[{"given":"Madhuri P.","family":"Karnik","sequence":"first","affiliation":[]},{"given":"D. V.","family":"Kodavade","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,3]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Fan, A., Grangier, D., Auli, M.: Controllable abstractive summarization. In: Proceedings of the 2nd Workshop on Neural Machine Translation and Generation, pp. 45\u201354, 20 July 2018. Association for Computational Linguistics (2018)","key":"30_CR1","DOI":"10.18653\/v1\/W18-2706"},{"unstructured":"Sutskever, I., Vinyals, O., Le., Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inform. Process. Syst. 27 (NIPS 2014), pp. 3104\u20133112 (2014)","key":"30_CR2"},{"unstructured":"Paulus, R., Xiong, C., Socher, R.: A deep reinforced model for abstractive summarization. In Proceedings of ICLR (2018)","key":"30_CR3"},{"doi-asserted-by":"crossref","unstructured":"Rush, A.M., Chopra, S., Weston, J.: A neural attention model for abstractive sentence summarization. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing (EMNLP 2015), pp. 379\u2013389 (2015)","key":"30_CR4","DOI":"10.18653\/v1\/D15-1044"},{"doi-asserted-by":"crossref","unstructured":"Chopra, S., Auli, M., Rush, A.M.: Abstractive sentence summarization with attentive recurrent neural networks. In: NAACL HLT 2016, The 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 93\u201398, 12\u201317 June 2016. San Diego California, USA (2016)","key":"30_CR5","DOI":"10.18653\/v1\/N16-1012"},{"doi-asserted-by":"crossref","unstructured":"Nallapati, R., Zhou, B., dos santos, C.N., Gulcehre, C., Xiang, B.: Abstractive text summarization using sequence-to-sequence rnns and beyond. In: Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning, CoNLL 2016, 11\u201312 August 2016, pp. 280\u2013290. Berlin, Germany (2016)","key":"30_CR6","DOI":"10.18653\/v1\/K16-1028"},{"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 ACL (2017)","key":"30_CR7","DOI":"10.18653\/v1\/P17-1099"},{"doi-asserted-by":"crossref","unstructured":"Liu, Y., Luo, Z., Zhu, K.: Controlling length in abstractive summarization using a convolutional neural network. In: EMNLP, pp. 4110\u20134119 (2018)","key":"30_CR8","DOI":"10.18653\/v1\/D18-1444"},{"doi-asserted-by":"crossref","unstructured":"Kikuchi, Y., Neubig, G., Sasano, R., Takamura, H., Okumura, M.: Controlling output length in neural encoder-decoders. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 1\u20135 Nov 2016, pp. 1328\u20131338 (2016)","key":"30_CR9","DOI":"10.18653\/v1\/D16-1140"},{"unstructured":"Schumann, R.: Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder. arXiv:1809.05233v2 [cs.CL] (2018)","key":"30_CR10"},{"doi-asserted-by":"crossref","unstructured":"Takase, S., Okazaki, N.: Positional Encoding to Control Output Sequence Length. arXiv:1904.07418v1 [cs.CL] (2019)","key":"30_CR11","DOI":"10.18653\/v1\/N19-1401"},{"unstructured":"Keskar, N., Bryan McCann, B., Lav, V., Xiong, C., Socher, R.: CTRL: A Conditional Transformer Language Model for Controllable Generation, arXiv:1909.05858v2 [cs.CL] (2019)","key":"30_CR12"},{"doi-asserted-by":"crossref","unstructured":"Mukherjee, R., Peruri, H.C., Vishnu, U., Goyal, P., Bhattacharya, S., Ganguly, N.: Read what you need: controllable Aspect-based Opinion Summarization of Tourist Reviews, SIGIR 2020, 25\u201330 July 2020, Virtual Event, China (2020)","key":"30_CR13","DOI":"10.1145\/3397271.3401269"},{"doi-asserted-by":"crossref","unstructured":"He, R., Lee, W.S., Ng, H.T., Dahlmeier, D.: An unsupervised neural attention model for aspect extraction. In: ACL (2017)","key":"30_CR14","DOI":"10.18653\/v1\/P17-1036"},{"unstructured":"Saito, I.: Length-controllable Abstractive Summarization by Guiding with Summary Prototype. arXiv:2001.07331 v1 [cs.CL] (2020)","key":"30_CR15"},{"doi-asserted-by":"crossref","unstructured":"Wu, C.-S., Liu, L., Liu, W., Stenetorp, P., Xiong, C.: Controllable Abstractive Dialogue Summarization with Sketch Supervision. arXiv:2105.14064v2 [cs.CL] (2021)","key":"30_CR16","DOI":"10.18653\/v1\/2021.findings-acl.454"},{"doi-asserted-by":"crossref","unstructured":"Chan, H.P., Wang, L., King, I.: Controllable summarization with constrained markov decision process. Trans. Assoc. Comput. Linguist. 9, 1213\u20131232 (2021)","key":"30_CR17","DOI":"10.1162\/tacl_a_00423"},{"doi-asserted-by":"crossref","unstructured":"Ficler, J., Goldberg, Y., Controlling linguistic style aspects in neural language generation. CoRR, abs\/1707.02633 (2017)","key":"30_CR18","DOI":"10.18653\/v1\/W17-4912"},{"doi-asserted-by":"publisher","unstructured":"Miao, N., Zhou, H., Mou, L., Yan, R., Li, L.: CGMH: constrained sentence generation by metropolis- hastings sampling. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, January 27\u2013Feb 1, 2019, pp. 6834\u20136842. Hawaii, USA (2019). https:\/\/doi.org\/10.1609\/aaai.v33i01.33016834","key":"30_CR19","DOI":"10.1609\/aaai.v33i01.33016834"},{"doi-asserted-by":"publisher","unstructured":"Schumann, R., Mou, L., Lu, Y., Vechtomova, O., Markert, K.: Discrete optimization for unsupervised sentence summarization with word-level extraction. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, ACL 2020, 5\u201310 July 2020, pp. 5032\u20135042. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.452","key":"30_CR20","DOI":"10.18653\/v1\/2020.acl-main.452"},{"unstructured":"He, J., Kry\u015bci\u0144ski, W., McCann, B., Rajani, N., Xiong, C.: CTRLSUM: Towards Generic Controllable Text summarization. arXiv:2012.04281v1 [cs.CL] (2020)","key":"30_CR21"},{"doi-asserted-by":"crossref","unstructured":"Li, C., Xu, W., Li, S., Gao, S.: Guiding generation for abstractive text summarization based on key information guide network. In: Proceedings of NAACL-HLT 2018, 1\u20136 June 2018, pp. 55\u201360. New Orleans, Louisiana, Association for Computational Linguistics (2018)","key":"30_CR22","DOI":"10.18653\/v1\/N18-2009"},{"doi-asserted-by":"crossref","unstructured":"Wang, H., Gao, Y., Bai, Y., Lapata, M., Huang, H.: exploring explainable selection to control abstractive summarization. In: The Thirty-Fifth AAAI Conference on Artificial .Intelligence (AAAI-21) (2021)","key":"30_CR23","DOI":"10.1609\/aaai.v35i15.17641"},{"doi-asserted-by":"crossref","unstructured":"Shen, X., Suzuki, J., Inui, K., Su, H., Klakow, D., Sekine, S.: Select and attend: towards controllable content selection in text generation. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pp. 579\u2013590 (2019)","key":"30_CR24","DOI":"10.18653\/v1\/D19-1054"},{"doi-asserted-by":"crossref","unstructured":"Nguyen, T., Luu, A.T., Lu, T., Quan, T.: Enriching and controlling global semantics for text summarization. In: Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pp.  9443\u20139456 (2021)","key":"30_CR25","DOI":"10.18653\/v1\/2021.emnlp-main.744"},{"doi-asserted-by":"crossref","unstructured":"Yu, Z., Wu, Z., Zheng, H., Yuan, Z.X., Fong, J., Su, W.: LenAtten: an effective length controlling unit for text summarization, Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021, pp. 363\u2013370 (2021)","key":"30_CR26","DOI":"10.18653\/v1\/2021.findings-acl.31"},{"doi-asserted-by":"crossref","unstructured":"Makino, T., Iwakura, T., Takamura, H., Okumura, M.: Global optimization under length constraint for neural text summarization. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 1039\u20131048. Association for Computational Linguistics, Florence, Italy (2019)","key":"30_CR27","DOI":"10.18653\/v1\/P19-1099"},{"doi-asserted-by":"crossref","unstructured":"Zheng, C., Cai, Y., Zhang, G., Li, Q.: Controllable abstractive sentence summarization with guiding entities. In: Proceedings of the 28th International Conference on Computational Linguistics, pp. 5668\u20135678. Barcelona, Spain (Online) (2020)","key":"30_CR28","DOI":"10.18653\/v1\/2020.coling-main.497"},{"doi-asserted-by":"crossref","unstructured":"Cao, S., Wang, L.: Inference time style control for summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 5942\u20135953 (2021)","key":"30_CR29","DOI":"10.18653\/v1\/2021.naacl-main.476"},{"doi-asserted-by":"crossref","unstructured":"Liu, Y., Jia, Q., Kenny, Q., Zhu: Length control in abstractive summarization by pretraining information selection. In: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers, pp. 6885\u20136895 (2022)","key":"30_CR30","DOI":"10.18653\/v1\/2022.acl-long.474"},{"doi-asserted-by":"crossref","unstructured":"Sarkhel, R., Keymanesh, M., Nandi, A., Parthasarathy, S.; Interpretable multi-headed attention for abstractive summarization at controllable lengths. In: Proceedings of the 28th International Conference on Computational Linguistics (2020)","key":"30_CR31","DOI":"10.18653\/v1\/2020.coling-main.606"},{"unstructured":"Hu, Z., Yang, Z., Liang, X., Salakhutdinov, R., Xing, E.P.: Toward the controlled generation of text. In: Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia Volume 70 (ICML2017), pp. 1587\u20131596 (2017)","key":"30_CR32"},{"doi-asserted-by":"crossref","unstructured":"Krishna, K., Srinivasan, B.V.: Generating topic-oriented summaries using neural attention. In: Proceedings of NAACL-HLT 2018, pp. 1697\u20131705. Association for Computational Linguistics (2018)","key":"30_CR33","DOI":"10.18653\/v1\/N18-1153"},{"unstructured":"Vaswani, A., et al.:. Attention is all you need. In: Advances in Neural Information Processing Systems 30 (NIPS 2017), pp. 5998\u20136008 (2017)","key":"30_CR34"},{"doi-asserted-by":"crossref","unstructured":"Och, F.J.: Minimum error rate training in statistical machine translation. In: Proceedings of ACL 2003, pp. 160\u2013167 (2003)","key":"30_CR35","DOI":"10.3115\/1075096.1075117"}],"container-title":["Lecture Notes in Networks and Systems","Intelligent Systems Design and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35507-3_30","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,2]],"date-time":"2023-06-02T21:27:41Z","timestamp":1685741261000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35507-3_30"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031355066","9783031355073"],"references-count":35,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35507-3_30","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"3 June 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISDA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Systems Design and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"isda2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.mirlabs.net\/isda22\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}