Computer Engineering and Applications ›› 2022, Vol. 58 ›› Issue (14): 27-39.DOI: 10.3778/j.issn.1002-8331.2111-0580
• Research Hotspots and Reviews • Previous Articles Next Articles
YU Qiang, LIN Min, LI Yanling
Online:
2022-07-15
Published:
2022-07-15
于强,林民,李艳玲
YU Qiang, LIN Min, LI Yanling. Review of Keyphrase Generation Based on Deep Learning[J]. Computer Engineering and Applications, 2022, 58(14): 27-39.
于强, 林民, 李艳玲. 基于深度学习的关键词生成研究综述[J]. 计算机工程与应用, 2022, 58(14): 27-39.
Add to citation manager EndNote|Ris|BibTeX
URL: http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2111-0580
[1] 胡少虎,张颖怡,章成志.关键词提取研究综述[J].数据分析与知识发现,2020,5(3):45-59. HU S H,ZHANG Y Y,ZHANG C Z.Review of keyword extraction studies[J].Data Analysis and Knowledge Discovery,2020,5(3):45-59. [2] LUHN H P.A statistical approach to mechanized encoding and searching of literary information[J].IBM Journal of Research and Development,1957,1(4):309-317. [3] MENG R,ZHAO S,HAN S,et al.Deep keyphrase generation[C]//Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers),2017:582-592. [4] YUAN X,WANG T,MENG R,et al.One size does not fit all:generating and evaluating variable number of keyphrases[C]//58th Annual Meeting of the Association for Computational Linguistics,2020. [5] CHO K,VAN MERRIENBOER B,GULCEHRE C,et al.Learning phrase representations using RNN encoder-decoder for statistical machine translation[J].arXiv:1406.1078,2014. [6] CHEN W,CHAN H P,LI P,et al.Exclusive hierarchical decoding for deep keyphrase generation[J].arXiv:2004. 08511,2020. [7] GU J,LU Z,LI H,et al.Incorporating copying mechanism in sequence-to-sequence learning[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers),2016:1631-1640. [8] HULTH A.Improved automatic keyword extraction given more linguistic knowledge[C]//Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing,2003. [9] NGUYEN T D,KAN M Y.Keyphrase extraction in scientific publications[C]//International Conference on Asian Digital Libraries.Berlin,Heidelberg:Springer,2007:317-326. [10] KIM S N,MEDELYAN O,KAN M Y,et al.Automatic keyphrase extraction from scientific articles[J].Language Resources and Evaluation,2013,47(3):723-742. [11] KRAPIVIN M,AUTAYEU A,MARCHESE M,et al.Keyphrases extraction from scientific documents:improving machine learning approaches with natural language processing[M]//CHOWDHURY G,KOO C,HUNTER J,ed.The role of digital libraries in a time of global change.Berlin,Heidelberg:Springer,2010:102-111. [12] WAN X,XIAO J.Single document keyphrase extraction using neighborhood knowledge[C]//Proceedings of the 23rd National Conference on Artificial Intelligence-Volume 2.Chicago,Illinois:AAAI Press,2008:855-860. [13] GALLINA Y,BOUDIN F,DAILLE B.KPTimes:a large-scale dataset for keyphrase generation on news documents[J].arXiv:1911.12559,2019. [14] WANG Y,LI J,CHAN H P,et al.Topic-aware neural keyphrase generation for social media language[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics,2019:2516-2526. [15] CANO E,BOJAR O.Keyphrase generation:a multi-aspect survey[C]//2019 25th Conference of Open Innovations Association(FRUCT),2019:85-94. [16] MIKOLOV T,CHEN K,CORRADO G,et al.Efficient estimation of word representations in vector space[J].arXiv:1301.3781,2013. [17] DAHLMEIER D,NG H T.A beam-search decoder for grammatical error correction[C]//Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning,2012:568-578. [18] WISEMAN S,RUSH A M.Sequence-to-sequence learning as beam-search optimization[J].arXiv:1606.02960,2016. [19] ZHANG Y,FANG Y,WEIDONG X.Deep keyphrase generation with a convolutional sequence to sequence model[C]//2017 4th International Conference on Systems and Informatics(ICSAI),2017:1477-1485. [20] ZHANG Y,XIAO W.Keyphrase generation based on deep seq2seq model[J].IEEE Access,2018,6:46047-46057. [21] TU Z,LU Z,LIU Y,et al.Modeling coverage for neural machine translation[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics(Volume 1:Long Papers),2016:76-85. [22] CHEN J,ZHANG X,WU Y,et al.Keyphrase generation with correlation constraints[J].arXiv:1808.07185,2018. [23] MIAO Y,GREFENSTETTE E,BLUNSOM P.Discovering discrete latent topics with neural variational inference[C]//Proceedings of the 34th International Conference on Machine Learning,2017:2410-2419. [24] ZENG J,LI J,SONG Y,et al.Topic memory networks for short text classification[J].arXiv:1809.03664,2018. [25] YE H,WANG L.Semi-supervised learning for neural keyphrase generation[C]//Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing,2018:4142-4153. [26] CHEN W,GAO Y,ZHANG J,et al.Title-guided encoding for keyphrase generation[C]//Proceedings of the AAAI Conference on Artificial Intelligence,2019:6268-6275. [27] CHEN W,CHAN H P,LI P,et al.An integrated approach for keyphrase generation via exploring the power of retrieval and extraction[C]//Proceedings of the 2019 Conference of the North,2019:2846-2856. [28] ZHAO J,BAO J,WANG Y,et al.SGG:learning to select,guide,and generate for keyphrase generation[J].arXiv:2105.02544,2021. [29] LIU R,LIN Z,WANG W.Keyphrase prediction with pre-trained language model[J].arXiv:2004.10462,2020. [30] DEVLIN J,CHANG M W,LEE K,et al.BERT:pre-training of deep bidirectional transformers for language understanding[J].arXiv:1810.04805,2018. [31] AHMAD W U,BAI X,LEE S,et al.Select,extract and generate:neural keyphrase generation with layer-wise coverage attention[J].arXiv:2008.01739,2020. [32] ALZAIDY R,CARAGEA C,GILES C L.Bi-LSTM-CRF sequence labeling for keyphrase extraction from scholarly documents[C]//The World Wide Web Conference on-WWW’19.San Francisco,CA,USA:ACM Press,2019:2551-2557. [33] XU Y,LUO Y,ZHOU Y,et al.Searching effective transformer for seq2seq keyphrase generation[C]//Natural Language Processing and Chinese Computing.Cham:Springer International Publishing,2021:86-97. [34] SHEN X,WANG Y,MENG R,et al.Unsupervised deep keyphrase generation[J].arXiv:2104.08729,2021. [35] HASAN K S,NG V.Conundrums in unsupervised keyphrase extraction:making sense of the state-of-the-art[C]//COLING 2010:Posters,2010:365-373. [36] BENNANI-SMIRES K,MUSAT C,HOSSMANN A,et al.Simple unsupervised keyphrase extraction using sentence embeddings[J].arXiv:1801.04470,2018. [37] CHAN H P,CHEN W,WANG L,et al.Neural keyphrase generation via reinforcement learning with adaptive rewards[J].arXiv:1906.04106,2019. [38] SEE A,LIU P J,MANNING C D.Get to the point:summarization with pointer-generator networks[J].arXiv:1704. 04368,2017. [39] LUO Y,XU Y,YE J,et al.Keyphrase generation with fine-grained evaluation-guided reinforcement learning[J].arXiv:2104.08799,2021. [40] VOORHEES R.Competency-based learning models:a necessary future[J].New Directions for Institutional Research,2001:5-13. [41] BAHULEYAN H,ASRI L E.Diverse keyphrase generation with neural unlikelihood training[J].arXiv:2010.07665,2020. [42] HABIBI M,POPESCU-BELIS A.Diverse keyword extraction from conversations[C]//Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics(Volume 2:Short Papers),2013:651-657. [43] ZESCH T,GUREVYCH I.Approximate matching for evaluating keyphrase extraction[C]//Proceedings of the International Conference RANLP-2009,2009:484-489. [44] YE J,GUI T,LUO Y,et al.One2Set:generating diverse keyphrases as a set[J].arXiv:2105.11134,2021. [45] MENG R,YUAN X,WANG T,et al.An empirical study on neural keyphrase generation[C]//Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies,2021:4985-5007. [46] 祖弦,谢飞,刘啸剑.融合词和文档嵌入的关键词抽取算法[J].计算机科学与探索,2021,15 (2):294-304. ZU X,XIE F,LIU X J.Keyphrase extraction combining word and document embeddings[J].Journal of Frontiers of Computer Science and Technology,2021,15(2):294-304. [47] 樊玮,刘欢,张宇翔.融合词向量与位置信息的关键词提取算法[J].计算机工程与应用,2020,56(5):179-185. FAN W,LIU H,ZHANG Y X.Keyphrase extraction algorithm integrating word embeddings and position information[J].Computer Engineering and Applications,2020,56(5):179-185. [48] 曾庆田,胡晓慧,李超.融合主题词嵌入和网络结构分析的主题关键词提取方法[J].数据分析与知识发现,2019(7):52-60. ZENG Q T,HU X H,LI C.Keyword extraction method based on keyword embedding and network structure analysis[J].Data Analysis and Knowledge Discovery,2019(7):52-60. [49] 黄佳佳,李鹏伟,彭敏,等.基于深度学习的主题模型研究[J].计算机学报,2020,43(5):827-855. HUANG J J,LI P W,PENG M,et al.Review of deep learning-based topic model[J].Chinese Journal of Computers,2020,43(5):827-855. [50] 李慧,田亚丹.一种层次化的科学知识结构发现方法[J].图书情报工作,2018,62(13):92-102. LI H,TIAN Y D.A hierarchical discovery method of scientific knowledge structure[J].Library and Information Service,2018,62(13):92-102. [51] HOYLE A M,GOEL P,RESNIK P.Improving neural topic models using knowledge distillation[C]//Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing(EMNLP),2020:1752-1771. [52] ZOU X.A survey on application of knowledge graph[J].Journal of Physics:Conference Series,2020,1487(1):012016. |
[1] | GAO Guangshang. Survey on Attention Mechanisms in Deep Learning Recommendation Models [J]. Computer Engineering and Applications, 2022, 58(9): 9-18. |
[2] | HE Qianqian, SUN Jingyu, ZENG Yazhu. Neighborhood Awareness Graph Neural Networks for Session-Based Recommendation [J]. Computer Engineering and Applications, 2022, 58(9): 107-115. |
[3] | ZHANG Xin, LIU Siyuan, XU Yanling. Knowledge-Aware Recommendation Algorithm Combined with Attention Mechanism [J]. Computer Engineering and Applications, 2022, 58(9): 168-174. |
[4] | CHEN Yidong, LU Zhonghua. Forecasting CPI Based on Convolutional Neural Network and Long Short-Term Memory Network [J]. Computer Engineering and Applications, 2022, 58(9): 256-262. |
[5] | WANG Lingmin, DUAN Jun, XIN Liwei. YOLOv5 Helmet Wear Detection Method with Introduction of Attention Mechanism [J]. Computer Engineering and Applications, 2022, 58(9): 303-312. |
[6] | ZHAO Dandan, HUANG Degen, MENG Jiana, GU Feng, ZHANG Pan. Chinese Named Entity Recognition by Integrating Multi-Heads Attention Mechanism and Character and Words Fusion [J]. Computer Engineering and Applications, 2022, 58(7): 142-149. |
[7] | CHEN Qiuchang, ZHAO Hui, ZUO Enguang, ZHAO Yuxia, WEI Wenyu. Implicit Sentiment Analysis Based on Context Aware Tree Recurrent Neutral Network [J]. Computer Engineering and Applications, 2022, 58(7): 167-175. |
[8] | WANG Xin, WANG Meili, BIAN Dangwei. Algorithm for Portrait Segmentation Combined with MobileNetv2 and Attention Mechanism [J]. Computer Engineering and Applications, 2022, 58(7): 220-228. |
[9] | YANG Xi, YAN Jie, WANG Wen, LI Shaoyi, LIN Jian. Research and Prospect of Brain-Inspired Model for Visual Object Recognition [J]. Computer Engineering and Applications, 2022, 58(7): 1-20. |
[10] | CHEN Zuozan, XU Bing, DING Xiaojun, GAN Jingzhong. Natural Scene Text Recognition Based on Encoder-Decoder Framework with Dual Supervision Mechanism [J]. Computer Engineering and Applications, 2022, 58(6): 128-133. |
[11] | CAO Yukun, SUN Tao. Chinese Event Argument Extraction Based on GLSTM and Attention [J]. Computer Engineering and Applications, 2022, 58(6): 157-163. |
[12] | LI Yanchen, ZHANG Xiaojun, ZHANG Minglu, SHEN Liangyi. Object Detection in Autonomous Driving Scene Based on Improved Efficientdet [J]. Computer Engineering and Applications, 2022, 58(6): 183-191. |
[13] | SHU Yali, ZHANG Guowei, WANG Bo, XU Xiaokang. Field Weed Identification Method Based on Deep Connection Attention Mechanism [J]. Computer Engineering and Applications, 2022, 58(6): 271-277. |
[14] | LIU Wenting, LU Xinming. Research Progress of Transformer Based on Computer Vision [J]. Computer Engineering and Applications, 2022, 58(6): 1-16. |
[15] | ZHAO Hong, FU Zhaoyang, ZHAO Fan. Microblog Sentiment Analysis Based on BERT and Hierarchical Attention [J]. Computer Engineering and Applications, 2022, 58(5): 156-162. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||