JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (7): 82-90.doi: 10.6040/j.issn.1671-9352.1.2020.047

Previous Articles    

Extractive-abstractive text automatic summary based on BERT-SUMOPN model

TAN Jin-yuan1, DIAO Yu-feng1, YANG Liang1, QI Rui-hua2, LIN Hong-fei1   

  1. 1. Information Retrieval Laboratory, Dalian University of Technology, Dalian 116024, Liaoning, China;
    2. Language Intelligence Research Center, Dalian University of Foreign Languages, Dalian 116024, Liaoning, China
  • Published:2021-07-19

PDF (PC)

501

Abstract: Extractive summaries have poor readability and accuracy, while abstractive summaries also have deficiencies in coherence and logic. In addition, the traditional models of the two summary methods are often insufficient and inaccurate for the vector representation of text. In response to the above problems, this paper proposes an extractive-abstractive summary method based on BERT-SUMOPN model. The model obtains the text vector through the BERT pre-trained language model, then extracts the key sentences in the text using the extractive summary model, and finally inputs the obtained key sentences into the pointer-generation network, and carries out the model through the EAC loss function for end-to-end training, combined with the coverage mechanism to reduce duplication and obtain summary results. The experimental results show that the BERT-SUMOPN model has achieved good results on the BIGPATENT patent dataset, and the ROUGE-1 and ROUGE-2 indicators have been improved by 3.3% and 2.5% respectively.

Key words: BERT pre-trained language model, Structured summary model, Pointer-generator network, EAC loss function

CLC Number: 

  • TP391.1
[1] 王凯祥.面向查询的自动文本摘要技术研究综述[J]. 计算机科学, 2018, 45(11A):12-16. WANG Kaixiang. Survey of query-oriented automatic summarization technology[J]. Computer Science, 2018, 45(11A):12-16.
[2] HSU W T, LIN C K, LEE MY, et al. A unified model for extractive and abstractive summarization using inconsistency loss[EB/OL].(2018-05-16)[2020-05-18]. https://arxiv.org/abs/1805.06266.
[3] LUHN H P. The automatic creation of literatureabstracts[J]. IBM Journal of Research and Development,1958,2(2):159-165.
[4] 张迎,王中卿,王红玲. 基于篇章主次关系的单文档抽取式摘要方法研究[J]. 中文信息学报, 2019, 33(8):67-76. ZHANG Ying, WANG Zhongqing, WANG Hongling. Single document extractive summarization with satellite and nuclear relations[J]. Journal of Chinese Information Processing, 2019, 33(8):67-76.
[5] 齐翌辰,王森淼,赵亚慧. 基于深度学习的中文抽取式摘要方法应用[J]. 科教导刊(中旬刊), 2019(14):67-70. QI Yichen, WANG Senmiao, ZHAO Yahui. Application of Chinese extractive abstraction method based on deep learning[J]. The Guide of Science & Education, 2019(14):67-70.
[6] 艾丽斯,唐卫红,傅云斌,等. 抽取式自动文本生成算法[J]. 华东师范大学学报(自然科学版), 2018, 2018(4):70-79. AI Lisi, TANG Weihong, FU Yunbin, et al. An algorithm for natural language generation via text extracting[J]. Journal of East China Normal University(Natural Sciences), 2018, 2018(4):70-79.
[7] NALLAPATI R, ZHAI F, ZHOU B. SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents[C] //Thirty-first AAAI Conference on Artificial Intelligence. [S.l] : AAAI, 2017.
[8] FANG Changjian, MU Dejun, DENG Zhenghong, et al. Word-sentence co-ranking for automatic extractive text summarization[J]. Expert Systems with Applications, 2017, 72:189-195.
[9] ZHANG Y, ER M J, ZHAO R, et al. Multiview convolutional neural networks for multidocument extractive summarization[J]. IEEE Transactions on Cybernetics, 2016, 47(10):3230-3242.
[10] 吴仁守,张宜飞,王红玲,等. 基于层次结构的生成式自动文摘[J]. 中文信息学报, 2019, 33(10):90-98. WU Renshou, ZHANG Yifei, WANG Hongling, et al. Abstractive summarization based on hierarchical structure[J]. Journal of Chinese Information Processing, 2019, 33(10):90-98.
[11] 田珂珂,周瑞莹,董浩业,等. 基于编码器共享和门控网络的生成式文本摘要方法[J]. 北京大学学报(自然科学版), 2020,56(1):61-67. TIAN Keke, ZHOU Ruiying, DONG Haoye, et al. An abstractive summarization method based on encoder-sharing and gated network[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2020, 56(1):61-67.
[12] 张敏,曾碧卿,韩旭丽,等.DAPC:结合双注意力和指针覆盖的文本摘要模型[J]. 计算机工程与应用,2020,5(8):149-157. ZHANG Min, ZENG Biqing, HAN Xuli, et al. DAPC:dual attention and pointer-coverage network based summarization model[J]. Computer Engineering and Applications, 2020, 5(8):149-157.
[13] CAO Ziqiang, WEI Furu, LI Wenjie, et al. Faithful to the original: fact aware neural abstractive summarization[C] //Thirty-second AAAI Conference on Artificial Intelligence. [S.l] : AAAI, 2018.
[14] LIU Linqing, LU Yao, YANG Min, et al. Generative adversarial network for abstractive text summarization[C] //Thirty-second AAAI Conference on Artificial Intelligence. [S.l] : AAAI, 2018.
[15] 吕瑞,王涛,曾碧卿,等. TSPT:基于预训练的三阶段复合式文本摘要模型[J]. 计算机应用研究,2020,37(10):2917-2921. LYU Rui, WANG Tao, ZENG Biqing, et al. TSPT: three-stage compound text summarization model based on pre-training[J]. Application Research of Computers, 2020, 37(10):2917-2921.
[16] 邱俊. 基于强化学习的混合式文本摘要模型[J].信息技术与信息化, 2019(1):67-70. QIU Jun. Hybrid text summarization model based on reinforcement learning [J]. Information Technology and Informatization, 2019(1):67-70.
[17] DEVLIN J, CHANG M W, LEE K, et al. Bert: pre-training of deep bidirectional transformers for language understanding[EB/OL].(2018-10-11)[2020-05-18]. https://arxiv.org/abs/1810.04805.
[18] LIU Y, TITOV I, LAPATA M. Single document summarization as tree induction[C] //Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies( Volume 1: Long and Short Papers). Minneapolis: ACL, 2019: 1745-1755.
[19] SEE A, LIU P J, MANNING C D. Get to the point: summarization with pointer-generator networks[C] // Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics(Volume 1: Long Papers). [S.l.] : ACL, 2017.
[20] SHARMA E, LI C, WANG L. Bigpatent: a large-scale dataset for abstractive and coherent summarization[EB/OL].(2019-06-10)[2020-05-18]. https://arxiv.org/abs/1906.03741v1.
[21] SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[EB/OL].(2014-09-10)[2020-05-18]. https://arxiv.org/abs/1409.3215v3.
[22] BOUTKAN F, RANZIJN J, RAU D, et al. Point-less: more abstractive summarization with pointer-generator networks[EB/OL].(2019-04-18)[2020-05-18]. https://arxiv.org/abs/1905.01975.
[23] GEHRMANN S, DENG Y, RUSH AM. Bottom-up abstractive summarization[EB/OL].(2018-08-31)[2020-05-18]. https://arxiv.org/abs/1808.10792v2.
[24] EDUNOV S, BAEVSKI A, AULI M. Pre-trained language model representations for language generation[EB/OL].(2019-03-22)[2020-05-18]. https://arxiv.org/abs/1903.09722v2.
[25] SUBRAMANIAN S, LI R, PILAULT J, et al. On extractive and abstractive neural document summarization with transformer language models[EB/OL].(2019-09-07)[2020-05-18]. https://arxiv.org/abs/1909.03186.
[26] CHEN Y C, BANSALM. Fast abstractive summarization with reinforce-selected sentence rewriting[EB/OL].(2018-05-28)[2020-05-18]. https://arxiv.org/abs/1805.11080v1.
[1] Kan XU,Rui-xin LIU,Hong-fei LIN,Hai-feng LIU,Jiao-jiao FENG,Jia-ping LI,Yuan LIN,Bo XU. Academic paper recommendation based on heterogeneous network embedding [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2020, 55(11): 35-45.
[2] Jia-qi WANG,Mu-yun YANG,Tie-jun ZHAO,Zhen-yu ZHAO. Construction of retrieval dataset of procuratorate legal documents [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2020, 55(7): 81-87.
[3] PANG Bo, LIU Yuan-chao. Fusion of pointwise and deep learning methods for passage ranking [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(3): 30-35.
[4] YANG Yan, XU Bing, YANG Mu-yun, ZHAO Jing-jing. An emotional classification method based on joint deep learning model [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(9): 19-25.
[5] HUANG Dong, XU Bo, XU Kan, LIN Hong-fei, YANG Zhi-hao. Short text clustering based on word embeddings and EMD [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 66-72.
[6] DU Man, XU Xue-ke, DU Hui, WU Da-yong, LIU Yue, CHENG Xue-qi. Emotion-specific word embedding learning for emotion classification [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2017, 52(7): 52-58.
[7] CAO Rong, HUANG Jin-zhu, YI Mian-zhu. Information retrieval: the final direction of human language technology research in DARPA [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(9): 11-17.
[8] FENG Guo-he, WANG Dan-di, LI Mei-chan. Text topic mining of archives research based on SVD [J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2016, 51(1): 95-100.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright 2018 © Editorial office of JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE)
Supported by Beijing Magtech Co.Ltd