Abstract
Text generation is an essential research area in artificial intelligence (AI) technology and natural language processing and provides key technical support for the rapid development of AI-generated content (AIGC). It is based on technologies such as natural language processing, machine learning, and deep learning, which enable learning language rules through training models to automatically generate text that meets grammatical and semantic requirements. In this paper, we sort and systematically summarize the main research progress in text generation and review recent text generation papers, focusing on presenting a detailed understanding of the technical models. In addition, several typical text generation application systems are presented. Finally, we address some challenges and future directions in AI text generation. We conclude that improving the quality, quantity, interactivity, and adaptability of generated text can help fundamentally advance AI text generation development.
摘要
文本生成是人工智能和自然语言处理的重要研究领域,为人工智能生成内容的快速发展提供了关键技术支撑。该任务基于自然语言处理、机器学习和深度学习等技术,通过训练模型学习语言规则,自动生成符合语法和语义要求的文本。本文对文本生成的主要研究进展进行梳理和系统性总结,对近几年文本生成相关文献进行综合调研,并详细介绍相关技术模型。此外,针对典型文本生成应用系统进行介绍。最后,对人工智能文本生成的挑战和未来研究方向进行分析和展望。得出以下结论,提高生成文本的质量、数量、交互性和适应性有助于从根本上推动人工智能文本生成的发展。
This is a preview of subscription content, log in via an institution to check access.
References
Adamopoulou E, Moussiades L, 2020. Chatbots: history, technology, and applications. Mach Learn Appl, 2:100006. https://doi.org/10.1016/j.mlwa.2020.100006
Akter M, Bansal N, Karmaker SK, 2022. Revisiting automatic evaluation of extractive summarization task: can we do better than ROUGE? Findings of the Association for Computational Linguistics, p.1547–1560. https://doi.org/10.18653/v1/2022.findings-acl.122
Albawi S, Mohammed TA, Al-Zawi S, 2017. Understanding of a convolutional neural network. Proc Int Conf on Engineering and Technology, p.1–6. https://doi.org/10.1109/ICEngTechnol.2017.8308186
Alexandr N, Irina O, Tatyana K, et al., 2021. Fine-tuning GPT-3 for Russian text summarization. Proc 5th Computational Methods in Systems and Software, p.748–757. https://doi.org/10.1007/978-3-030-90321-3_61
Barker E, Paramita ML, Aker A, et al., 2016. The SENSEI annotated corpus: human summaries of reader comment conversations in on-line news. Proc 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p.42–52. https://doi.org/10.18653/v1/W16-3605
Barrera A, Verma R, 2012. Combining syntax and semantics for automatic extractive single-document summarization. Proc 13th Int Conf on Computational Linguistics and Intelligent Text Processing, p.366–377. https://doi.org/10.1007/978-3-642-28601-8_31
Birjali M, Kasri M, Beni-Hssane A, 2021. A comprehensive survey on sentiment analysis: approaches, challenges and trends. Knowl-Based Syst, 226:107134. https://doi.org/10.1016/j.knosys.2021.107134
Cai D, Wang Y, Liu L, et al., 2022. Recent advances in retrieval-augmented text generation. Proc 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.3417–3419. https://doi.org/10.1145/3477495.3532682
Cao JR, Wang CW, 2018. Social media text generation based on neural network model. Proc 2nd Int Conf on Computer Science and Artificial Intelligence, p.58–61. https://doi.org/10.1145/3297156.3297206
Chen YC, Gan Z, Cheng Y, et al., 2020. Distilling knowledge learned in BERT for text generation. Proc 58th Annual Meeting of the Association for Computational Linguistics, p.7893–7905. https://doi.org/10.18653/v1/2020.acl-main.705
Chen ZH, Shen YL, Song Y, et al., 2021. Cross-modal memory networks for radiology report generation. Proc 59th Annual Meeting of the Association for Computational Linguistics and the 11th Int Joint Conf on Natural Language Processing, p.5904–5914. https://doi.org/10.18653/v1/2021.acl-long.459
Christian H, Agus MP, Suhartono D, 2016. Single document automatic text summarization using term frequency-inverse document frequency (TF-IDF). ComTech, 7(4):285–294. https://doi.org/10.21512/comtech.v7i4.3746
Coenen A, Davis L, Ippolito D, et al., 2021. Wordcraft: a human-AI collaborative editor for story writing. https://doi.org/10.48550/arXiv.2107.07430
Croft R, Newlands D, Chen ZY, et al., 2021. An empirical study of rule-based and learning-based approaches for static application security testing. Proc 15th ACM/IEEE Int Symp on Empirical Software Engineering and Measurement, Article 8. https://doi.org/10.1145/3475716.3475781
Dathathri S, Madotto A, Lan J, et al., 2020. Plug and play language models: a simple approach to controlled text generation. Proc 8th Int Conf on Learning Representations.
Devlin KJ, Chang MW, Lee K, 2019. BERT: pre-training of deep bidirectional Transformers for language understanding. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.4171–4186. https://doi.org/10.18653/v1/N19-1423
Dey R, Salem FM, 2017. Gate-variants of gated recurrent unit (GRU) neural networks. Proc IEEE 60th Int Midwest Symp on Circuits and Systems, p.1597–1600. https://doi.org/10.1109/MWSCAS.2017.8053243
Ding HJ, Xu XL, 2023. SAN-T2T: an automated table-to-text generator based on selective attention network. Nat Lang Eng, First View. https://doi.org/10.1017/S135132492300013X
Doush IA, Alkhateeb F, Albsoul A, 2017. AraDaisy: a system for automatic generation of ARABIC daisy books. Int J Comput Appl Technol, 55(4):322–333. https://doi.org/10.1504/IJCAT.2017.086007
Dowling M, Lucey B, 2023. ChatGPT for (finance) research: the bananarama conjecture. Financ Res Lett, 53:103662. https://doi.org/10.1016/j.frl.2023.103662
Evtikhiev M, Bogomolov E, Sokolov Y, et al., 2023. Out of the BLEU: how should we assess quality of the code generation models? J Syst Softw, 203:111741. https://doi.org/10.1016/j.jss.2023.111741
Fan A, Gardent C, Braud C, et al., 2019. Using local knowledge graph construction to scale Seq2Seq models to multi-document inputs. Proc Conf on Empirical Methods in Natural Language Processing and the 9th Int Joint Conf on Natural Language Processing, p.4186–4196. https://doi.org/10.18653/v1/D19-1428
Fei H, Li CL, Ji DH, et al., 2022. Mutual disentanglement learning for joint fine-grained sentiment classification and controllable text generation. Proc 45th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1555–1565. https://doi.org/10.1145/3477495.3532029
Feng YS, Lapata M, 2013. Automatic caption generation for news images. IEEE Trans Patt Anal Mach Intell, 35(4):797–812. https://doi.org/10.1109/TPAMI.2012.118
Floridi L, Chiriatti M, 2020. GPT-3: its nature, scope, limits, and consequences. Minds Mach, 30(4):681–694. https://doi.org/10.1007/s11023-020-09548-1
Gao S, Ren ZC, Zhao YH, et al., 2019. Product-aware answer generation in e-commerce question-answering. Proc 12th ACM Int Conf on Web Search and Data Mining, p.429–437. https://doi.org/10.1145/3289600.3290992
Garbacea C, Mei QZ, 2020. Neural language generation: formulation, methods, and evaluation. https://doi.org/10.48550/arXiv.2007.15780
Glennie R, Adam T, Leos-Barajas V, et al., 2023. Hidden Markov models: pitfalls and opportunities in ecology. Methods Ecol Evol, 14(1):43–56. https://doi.org/10.1111/2041-210X.13801
Goodfellow I, Pouget-Abadie J, Mirza M, et al., 2020. Generative adversarial networks. Commun ACM, 63(11):139–144. https://doi.org/10.1145/3422622
Guo ZX, Yan MX, Qi JX, et al., 2023. Few-shot table-to-text generation with prompt planning and knowledge memorization. https://doi.org/10.48550/arXiv.2302.04415
Gupta A, Chugh D, Anjum, 2022. Automated news summarization using transformers. Int Conf on Sustainable Advanced Computing, p.249–259. https://doi.org/10.1007/978-981-16-9012-9_21
He XL, Xu QK, Zeng Y, et al., 2022. CATER: intellectual property protection on text generation APIs via conditional watermarks. Proc 36th Conf on Neural Information Processing Systems.
Hua XY, Wang L, 2020. PAIR: planning and iterative refinement in pre-trained Transformers for long text generation. Proc Conf on Empirical Methods in Natural Language Processing, p.781–793. https://doi.org/10.18653/v1/2020.emnlp-main.57
Imam AT, Rousan T, Aljawarneh S, 2014. An expert code generator using rule-based and frames knowledge representation techniques. Proc 5th Int Conf on Information and Communication Systems, p.1–6. https://doi.org/10.1109/IACS.2014.6841951
Jayasiriwardene TD, Ganegoda GU, 2020. Keyword extraction from tweets using NLP tools for collecting relevant news. Proc Int Research Conf on Smart Computing and Systems Engineering, p.129–135. https://doi.org/10.1109/SCSE49731.2020.9313024
Jin Q, Dhingra B, Liu ZP, et al., 2019. PubMedQA: a dataset for biomedical research question answering. Proc 9th Int Joint Conf on Natural Language Processing, p.2567–2577. https://doi.org/10.18653/v1/D19-1259
Jungwirth D, Haluza D, 2023. Artificial intelligence and the sustainable development goals: an exploratory study in the context of the society domain. J Softw Eng Appl, 16(4):91–112. https://doi.org/10.4236/jsea.2023.164006
Karakoylu PE, Kural AG, Gulden S, 2020. The effect of artificial intelligence (AI) on new product development (NPD): a future scenario. IOP Conf Ser Mater Sci Eng, 960:022026. https://doi.org/10.1088/1757-899X/960/2/022026
Keskar NS, McCann B, Varshney LR, et al., 2019. CTRL: a conditional transformer language model for controllable generation. https://doi.org/10.48550/arXiv.1909.05858
King MR, 2023. A place for large language models in scientific publishing, apart from credited authorship. Cell Mol Bioeng, 16(2):95–98. https://doi.org/10.1007/s12195-023-00765-z
King MR, Chat GPT, 2023. A conversation on artificial intelligence, chatbots, and plagiarism in higher education. Cell Mol Bioeng, 16(1):1–2. https://doi.org/10.1007/s12195-022-00754-8
Kingma DP, Welling M, 2014. Auto-encoding variational Bayes. https://arxiv.org/abs/1312.6114
Koncel-Kedziorski R, Bekal D, Luan Y, et al., 2019. Text generation from knowledge graphs with graph transformers. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.2284–2293. https://doi.org/10.18653/v1/N19-1238
Koupaee M, Wang WY, 2018. WikiHow: a large scale text summarization dataset. https://doi.org/10.48550/arXiv.1810.09305
Kraus S, Castellanos I, Albermann M, et al., 2016. Using Arden syntax for the generation of intelligent intensive care discharge letters. Stud Health Technol Inform, 228:471–475. https://doi.org/10.3233/978-1-61499-678-1-471
Lai V, Smith-Renner A, Zhang K, et al., 2022. An exploration of post-editing effectiveness in text summarization. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.475–493. https://doi.org/10.18653/v1/2022.naacl-main.35
Lee JS, Hsiang J, 2020. Patent claim generation by fine-tuning OpenAI GPT-2. World Patent Inform, 62:101983. https://doi.org/10.1016/j.wpi.2020.101983
Leppänen L, Munezero M, Granroth-Wilding M, et al., 2017. Data-driven news generation for automated journalism. Proc 10th Int Conf on Natural Language Generation, p.188–197. https://doi.org/10.18653/v1/W17-3528
Lewis M, Liu YH, Goyal N, et al., 2020. BART: denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. Proc 58th Annual Meeting of the Association for Computational Linguistics, p.7871–7880. https://doi.org/10.18653/v1/2020.acl-main.703
Li B, Yang P, Zhao HL, et al., 2023. Hierarchical sliding inference generator for question-driven abstractive answer summarization. ACM Trans Inform Syst, 41(1):7. https://doi.org/10.1145/3511891
Li JJ, Song HZ, Li J, 2022. Transformer-based question text generation in the learning system. Proc 6th Int Conf on Innovation in Artificial Intelligence, p.50–56. https://doi.org/10.1145/3529466.3529484
Li YJ, Choi D, Chung J, et al., 2022. Competition-level code generation with AlphaCode. Science, 378(6624):1092–1097. https://doi.org/10.1126/science.abq1158
Liang ZY, Du JP, Shao YX, et al., 2021. Gated graph neural attention networks for abstractive summarization. Neurocomputing, 431:128–136. https://doi.org/10.1016/j.neucom.2020.09.066
Lin J, Madnani N, Dorr BJ, 2010. Putting the user in the loop: interactive maximal marginal relevance for query-focused summarization. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.305–308.
Lin JY, Sun X, Ma SM, et al., 2018. Global encoding for abstractive summarization. Proc 56th Annual Meeting of the Association for Computational Linguistics, p.163–169. https://doi.org/10.18653/v1/P18-2027
Liu TY, Wang KX, Sha L, et al., 2018. Table-to-text generation by structure-aware Seq2Seq learning. Proc 32nd AAAI Conf on Artificial Intelligencee, 32(1). https://doi.org/10.1609/aaai.v32i1.11925
Liu Y, Lapata M, 2019a. Hierarchical Transformers for multi-document summarization. Proc 57th Annual Meeting of the Association for Computational Linguistics, p.5070–5081. https://doi.org/10.18653/v1/P19-1500
Liu Y, Lapata M, 2019b. Text summarization with pre-trained encoders. Proc Conf on Empirical Methods in Natural Language Processing and the 9th Int Joint Conf on Natural Language Processing, p.3730–3740. https://doi.org/10.18653/v1/D19-1387
Liu Y, Wan Y, He LF, et al., 2021. KG-BART: knowledge graph-augmented BART for generative commonsense reasoning. Proc 35th AAAI Conf on Artificial Intelligence, p.6418–6425. https://doi.org/10.1609/aaai.v35i7.16796
Liu YH, Ott M, Goyal N, et al., 2019. RoBERTa: a robustly optimized BERT pretraining approach. https://doi.org/10.48550/arXiv.1907.11692
Lund BD, Wang T, 2023. Chatting about ChatGPT: how may AI and GPT impact academia and libraries? Lib Hi Tech News, 40(3):26–29. https://doi.org/10.1108/LHTN-01-2023-0009
Luo Y, Zhao SL, Li XC, et al., 2016. Text keyword extraction method based on word frequency statistics. J Comput Appl, 36(3):718–725 (in Chinese). https://doi.org/10.11772/j.issn.1001-9081.2016.03.718
Ma C, Zhang S, Shen GH, et al., 2022. Switch-GPT: an effective method for constrained text generation under few-shot settings (student abstract). Proc 36th AAAI Conf on Artificial Intelligence, p.13011–13012. https://doi.org/10.1609/aaai.v36i11.21642
Meng R, Zhao SQ, Han SG, et al., 2017. Deep keyphrase generation. Proc 55th Annual Meeting of the Association for Computational Linguistics, p.582–592. https://doi.org/10.18653/v1/P17-1054
Miller D, 2019. Leveraging BERT for extractive text summarization on lectures. https://doi.org/10.48550/arXiv.1906.04165
Mukherjee S, 2021. Sentiment analysis. In: Mukherjee S (Ed.), MLNET Revealed: Simple Tools for Applying Machine Learning to Your Applications. Apress, Berkeley, p.113–127. https://doi.org/10.1007/978-1-4842-6543-7_7
Oh C, Choi J, Lee S, et al., 2020. Understanding user perception of automated news generation system. Proc CHI Conf on Human Factors in Computing Systems, p.1–13. https://doi.org/10.1145/3313831.3376811
Omar R, Mangukiya O, Kalnis P, et al., 2023. ChatGPT versus traditional question answering for knowledge graphs: current status and future directions towards knowledge graph chatbots. https://doi.org/10.48550/arXiv.2302.06466
Özateş SB, Özgür A, Radev D, 2016. Sentence similarity based on dependency tree kernels for multi-document summarization. Proc 10th Int Conf on Language Resources and Evaluation, p.2833–2838.
Palangi H, Deng L, Shen YL, et al., 2016. Deep sentence embedding using long short-term memory networks: analysis and application to information retrieval. IEEE/ACM Trans Audio Speech Lang Process, 24(4):694–707. https://doi.org/10.1109/TASLP.2016.2520371
Procter R, Arana-Catania M, He YL, et al., 2023. Some observations on fact-checking work with implications for computational support. https://doi.org/10.48550/arXiv.2305.02224
Rudin C, 2019. Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intell, 1(5):206–215. https://doi.org/10.1038/s42256-019-0048-x
Saeed A, Ilić S, Zangerle E, 2019. Creative GANs for generating poems, lyrics, and metaphors. https://doi.org/10.48550/arXiv.1909.09534
See A, Liu PJ, Manning CD, 2017. Get to the point: summarization with pointer-generator networks. Proc 55th Annual Meeting of the Association for Computational Linguistics, p.1073–1083. https://doi.org/10.18653/v1/P17-1099
Selvadurai N, Matulionyte R, 2020. Reconsidering creativity: copyright protection for works generated using artificial intelligence. J Intellect Prop Law Pract, 15(7):536–543. https://doi.org/10.1093/jiplp/jpaa062
Senftleben M, Buijtelaar L, et al., 2020. Robot creativity: an incentive-based neighbouring rights approach. Eur Intellect Prop Rev, 42(12):717. https://doi.org/10.2139/ssrn.3707741
Smith S, Patwary M, Norick B, et al., 2022. Using DeepSpeed and megatron to train Megatron-Turing NLG 530B, a large-scale generative language model. https://doi.org/10.48550/arXiv.2201.11990
Stevens K, 2022. Retrospective enhancement of bibliographic records with tables of contents and summary data and the effect on collection usage. J Aust Lib Inform Assoc, 71(4):379–387. https://doi.org/10.1080/24750158.2022.2135359
Sun XF, Meng YX, Ao X, et al., 2022. Sentence similarity based on contexts. Trans Assoc Comput Ling, 10:573–588. https://doi.org/10.1162/tacl_a_00477
Surameery NMS, Shakor MY, 2023. Use ChatGPT to solve programming bugs. Int J Inform Technol Comput Eng, 3(1):17–22. https://doi.org/10.55529/ijitc.31.17.22
Svyatkovskiy A, Deng SK, Fu SY, et al., 2020. IntelliCode compose: code generation using transformer. Proc 28th ACM Joint Meeting on European Software Engineering Conf and Symp on the Foundations of Software Engineering, p.1433–1443. https://doi.org/10.1145/3368089.3417058
Thoppilan R, De Freitas D, Hall J, et al., 2022. LaMDA: language models for dialog applications. https://doi.org/10.48550/arXiv.2201.08239
Tian YF, Peng NY, 2022. Zero-shot sonnet generation with discourse-level planning and aesthetics features. Proc Conf of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, p.3587–3597. https://doi.org/10.18653/v1/2022.naacl-main.262
Tomczak JM, Welling M, 2016. Improving variational autoencoders using householder flow. https://doi.org/10.48550/arXiv.1611.09630
Tran OT, Bui VT, 2021. Neural text normalization in speech-to-text systems with rich features. Appl Artif Intell, 35(3):193–205. https://doi.org/10.1080/08839514.2020.1842108
Trivedi A, Pant N, Shah P, et al., 2018. Speech to text and text to speech recognition systems—a review. IOSR J Comput Eng, 20(2):36–43. https://doi.org/10.9790/0661-2002013643
van der Lee C, Gatt A, van Miltenburg E, et al., 2019. Best practices for the human evaluation of automatically generated text. Proc 12th Int Conf on Natural Language Generation, p.355–368. https://doi.org/10.18653/v1/W19-8643
Vaswani A, Shazeer N, Parmar N, et al., 2017. Attention is all you need. Proc 31st Int Conf on Neural Information Processing Systems, p.6000–6010. https://doi.org/10.5555/3295222.3295349
Vodolazova T, Lloret E, 2019. The impact of rule-based text generation on the quality of abstractive summaries. Proc Int Conf on Recent Advances in Natural Language Processing, p.1275–1284. https://doi.org/10.26615/978-954-452-056-4-146
Wang CH, Tang Y, Ma XT, et al., 2020. FAIRSEQ S2T: fast speech-to-text modeling with FAIRSEQ. Proc 1st Conf of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th Int Joint Conf on Natural Language Processing: System Demonstrations, p.33–39.
Wang TM, Wan XQ, Jin HQ, 2020. AMR-to-text generation with graph Transformer. Trans Assoc Comput Ling, 8:19–33. https://doi.org/10.1162/tacl_a_00297
Wang WM, Li Z, Wang JW, et al., 2017. How far we can go with extractive text summarization? Heuristic methods to obtain near upper bounds. Exp Syst Appl, 90:439–463. https://doi.org/10.1016/j.eswa.2017.08.040
Xu HH, Savelka J, Ashley KD, 2021. Accounting for Sentence Position and Legal Domain Sentence Embedding in Learning to Classify Case Sentences. IOS Press, Vilnius, Lithuania, p.33–42.
Yellin DM, Weiss G, 2021. Synthesizing context-free grammars from recurrent neural networks. Proc 27th Int Conf on Tools and Algorithms for the Construction and Analysis of Systems, p.351–369. https://doi.org/10.1007/978-3-030-72016-2_19
Yu X, Vu NT, Kuhn J, 2019. Learning the dyck language with attention-based Seq2Seq models. Proc ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, p.138–146. https://doi.org/10.18653/v1/W19-4815
Yu Y, Si XS, Hu CH, et al., 2019. A review of recurrent neural networks: LSTM cells and network architectures. Neur Comput, 31(7):1235–1270. https://doi.org/10.1162/neco_a_01199
Zhang TY, Kishore V, Wu F, et al., 2020. BERTScore: evaluating text generation with BERT. Proc 8th Int Conf on Learning Representations.
Zhang XY, Zou YY, Zhang HN, et al., 2022a. Automatic product copywriting for e-commerce. Proc 36th AAAI Conf on Artificial Intelligence, p.12423–12431. https://doi.org/10.1609/aaai.v36i11.21508
Zhang XY, Shen K, Zhang C, et al., 2022b. Scenario-based multi-product advertising copywriting generation for e-commerce. https://doi.org/10.48550/arXiv.2205.10530
Zhang YQ, Huang ML, 2019. Overview of the NTCIR-14 short text generation subtask: emotion generation challenge. Proc 14th NTCIR Conf on Evaluation of Information Access Technologies, p.316–327.
Zhang ZY, Han X, Liu ZY, et al., 2019. ERNIE: enhanced language representation with informative entities. Proc 57th Annual Meeting of the Association for Computational Linguistics, p.1441–1451. https://doi.org/10.18653/v1/P19-1139
Zhao GZ, Yang P, 2022. Table-based fact verification with self-labeled keypoint alignment. Proc 29th Int Conf on Computational Linguistics, p.1401–1411.
Zhao L, Xu JJ, Lin JY, et al., 2020. Graph-based multi-hop reasoning for long text generation. https://doi.org/10.48550/arXiv.2009.13282
Zhao W, Peyrard M, Liu F, et al., 2019. MoverScore: text generation evaluating with contextualized embeddings and earth mover distance. Proc Conf on Empirical Methods in Natural Language Processing and the 9th Int Joint Conf on Natural Language Processing, p.563–578. https://doi.org/10.18653/v1/D19-1053
Zhao W, Strube M, Eger S, 2022. DiscoScore: evaluating text generation with BERT and discourse coherence. Proc 17th Conf of the European Chapter of the Association for Computational Linguistics, p.3865–3883. https://doi.org/10.18653/v1/2023.eacl-main.278
Zheng LX, Ma S, Chen ZX, et al., 2021. Ensuring the correctness of regular expressions: a review. Int J Autom Comput, 18(4):521–535. https://doi.org/10.1007/s11633-021-1301-4
Zhu CG, Yang ZY, Gmyr R, et al., 2021. Leveraging lead bias for zero-shot abstractive news summarization. Proc 44th Int ACM SIGIR Conf on Research and Development in Information Retrieval, p.1462–1471. https://doi.org/10.1145/3404835.3462846
Zhu QH, Luo JX, 2023. Generative design ideation: a natural language generation approach. In: Gero JS (Ed.), Design Computing and Cognition’22. Springer, Cham, p.39–50. https://doi.org/10.1007/978-3-031-20418-0_3
Zogopoulos V, Geurts E, Gors D, et al., 2022. Authoring tool for automatic generation of augmented reality instruction sequence for manual operations. Procedia CIRP, 106:84–89. https://doi.org/10.1016/j.procir.2022.02.159
Author information
Authors and Affiliations
Contributions
Bing LI designed the research and drafted the paper. Peng YANG oversaw and led the planning and execution of the study. Yuankang SUN, Zhongjian HU, and Meng YI collected the information and revised the paper.
Corresponding author
Ethics declarations
All the authors declare that they have no conflict of interest.
Additional information
Project supported by the National Natural Science Foundation of China (No. 62272100), the Consulting Project of Chinese Academy of Engineering (No. 2023-XY-09), the Major Project of the National Social Science Fund of China (No. 21ZD11), and the Fundamental Research Funds for the Central Universities, China
Rights and permissions
About this article
Cite this article
Li, B., Yang, P., Sun, Y. et al. Advances and challenges in artificial intelligence text generation. Front Inform Technol Electron Eng 25, 64–83 (2024). https://doi.org/10.1631/FITEE.2300410
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1631/FITEE.2300410