Abstract
A global and local information extraction model incorporating selection mechanism is presented to address the concerns of insufficient semantic coding and redundant semantic information in the abstractive summary. The model, unlike single coding, encodes the source text twice. The global semantic information is extracted by the encoder based on Bidirectional Gated Recurrent Unit network, while the local feature vector is extracted by the encoder based on Dilated Convolution Network. The selection gate is in charge of filtering out redundant data. The output of the two encoders is fused as the input of the decoder to improve the feature representation at the source to generate diversified summaries. The model has good performance and effectively enhances the quality of summary, according on the experimental findings on two tough datasets, CNN/DailyMail and DUC 2004.
Similar content being viewed by others
References
Bahdanau D, Cho K, Bengio Y (2014) Neural machine translation by jointly learning to align and translate. Computer Science
Bansal M, Lobiyal D K (2021) Multilingual sequence to sequence convolutional machine translation. Multimed Tools Applic 80(25):33701–33726. https://doi.org/10.1007/s11042-021-11345-6
Chen L C, Papandreou G, Schroff F, Adam H (2017) Rethinking atrous convolution for semantic image segmentation
Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision - ECCV 2018, PT VII, vol 11211. Springer International Publishing AG, Cham, pp 833–851
Chopra S, Auli M, Rush A M (2016) Abstractive sentence summarization with attentive recurrent neural networks. In: Conference of the North American chapter of the association for computational linguistics: human language technologies
Ding J, Li Y, Wang J (2019) Automatic summarization method of short text based on dual encoder. Comput Applic 39(12):6
Fudholi D H, Nayoan R A N, Hidayatullah A F, Arianto D B (2022) A hybrid cnn-bilstm model for drug named entity recognition. J Eng Sci Technol 17(1):730–744
Gambhir M, Gupta V Deep learning-based extractive text summarization with word-level attention mechanism. Multimedia Tools And Applications
Gao W, Ma H, Li D, Yu P (2021) Research and implementation of chinese text summarization technology based on dual encoder. Comput Eng Des 42(9):9
Gehring J, Auil M, Grangier D, Yarats D, Dauphin Y N (2017) Convolutional sequence to sequence learning. In: Precup D, Teh YW (eds) International conference on machine learning, vol 70. JMLR-Journal Machine Learning Research, San Diego
Ghosh R A recurrent neural network based deep learning model for text and non-text stroke classification in online handwritten devanagari document. Multimedia Tools And Applications. https://doi.org/10.1007/s11042-022-12767-6
He J, Zhang S, Yang M, Shan Y, Huang T (2022) Bdcn: Bi-directional cascade network for perceptual edge detection. IEEE Trans Pattern Anal Mach Intell 44:100–113. https://doi.org/10.1109/TPAMI.2020.3007074
Hermann K M, Kocisky T, Grefenstette E, Espeholt L, Kay W, Suleyman M, Blunsom P (2015) Teaching machines to read and comprehend. In: Cortes C, Lawrence ND, Lee DD, Sugiyama M, Garnett R (eds) Advances in neural information processing systems 28 (NIPS 2015), vol 28. Neural Information Processing Systems (NIPS), La Jolla
Hinton G E, Salakhutdinov R R (2006) Reducing the dimensionality of data with neural networks. Science 313:504–507. https://doi.org/10.1126/science.1127647
Hu B, Chen Q, Zhu F (2015) Lcsts: a large scale Chinese short text summarization dataset
Huang Y, Yu Z, Guo J, Xiang Y, Yu Z, Xian Y (2022) Abstractive document summarization via multi-template decoding. Appl Intell 52:9650–9663. https://doi.org/10.1007/s10489-021-02607-9
Kalchbrenner N, Espeholt L, Simonyan K, Oord A, Graves A, Kavukcuoglu K (2016) Neural machine translation in linear time
Kedzie C, Mckeown K, H D III (2018) Content selection in deep learning models of summarization
Kim Y (2014) Convolutional neural networks for sentence classification. Eprint Arxiv
Liang J, Du M (2022) Two-way neural network chinese-english machine translation model fused with attention mechanism. Sci Program, 2022. https://doi.org/10.1155/2022/1270700
Liang Z, Du J, Li C (2020) Abstractive social media text summarization using selective reinforced seq2seq attention model. Neurocomputing 410:432–440. https://doi.org/10.1016/j.neucom.2020.04.137
Liao K, Lebanoff L, Liu F (2018) Abstract meaning representation for multi-document summarization
Lin C-Y (2004) Rouge: a package for automatic evaluation of summaries. In: Workshop on text summarization branches out, post-conference workshop of ACL 2004, Barcelona, Spain, pp 74–81. https://www.microsoft.com/en-us/research/publication/rouge-a-package-for-automatic-evaluation-of-summaries/
Lin H, Ng V (2019) Abstractive summarization: a survey of the state of the art. In: Thirty-Third AAAI conference on artificial intelligence / thirty-first innovative applications of artificial intelligence conference / Ninth AAAI symposium on educational advances in artificial intelligence, Assoc Advancement Artificial Intelligence. Assoc Advancement Artificial Intelligence, Palo Alto, pp 9815–9822
Lin J, Sun X, Ma S, Su Q (2018) Global encoding for abstractive summarization. In: Gurevych I, Miyao Y (eds) Proceedings of the 56th annaul meeting of the association for computational linguistics, vol 2. Assoc Computational Linguistics-ACL, Stroudsburg, pp 163–169
Liu S, Huang D, Wang Y (2018) Receptive field block net for accurate and fast object detection. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer Vision - ECCV 2018, PT XI, vol 11215. Springer International Publishing AG, Cham, pp 404–419
Ma T, Pan Q, Rong H, Qian Y, Tian Y, Al-Nabhan N (2022) T-bertsum: topic-aware text summarization based on bert. IEEE Trans Comput Soc Syst 9(3):879–890. https://doi.org/10.1109/TCSS.2021.3088506
Nallapati R, Zhou B, Santos C, Gulcehre C, Bing X (2016) Abstractive text summarization using sequence-to-sequence rnns and beyond
Nallapati R, Zhai F, Zhou B (2017) Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Thirty-First AAAI conference on artificial intelligence, Assoc Advancement Artificial Intelligence. Assoc Advancement Artificial Intelligence, PALO ALTO, pp 3075–3081
Napoles C, Gormley M, Durme B V (2012) Annotated gigaword. In: Proceedings of the joint workshop on automatic knowledge base construction and web-scale knowledge extraction
Niu G, Xu H, He B, Xiao X, Wu H, Gao S (2019) Enhancing local feature extraction with global representation for neural text classification. In: Inui K, Jiang J, Ng V, Wan X (eds) Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019. Association for Computational Linguistics, pp 496–506, DOI https://doi.org/10.18653/v1/D19-1047
Oord A, Dieleman S, Zen H, Simonyan K, Kavukcuoglu K (2016) Wavenet: a generative model for raw audio
Over P, Dang H, Harman D (2007) Duc in context. Inf Process Manag 43:1506–1520. https://doi.org/10.1016/j.ipm.2007.01.019
Qiu D, Yang B (2022) Text summarization based on multi-head self-attention mechanism and pointer network. Complex Intell Syst 8:555–567. https://doi.org/10.1007/s40747-021-00527-2
Rahman M M, Siddiqui F H (2021) Multi-layered attentional peephole convolutional lstm for abstractive text summarization. ETRI J 43 (2):288–298. https://doi.org/10.4218/etrij.2019-0016
Rani R, Lobiyal D K (2021) An extractive text summarization approach using tagged-lda based topic modeling. Multimed Tools Applic 80(3):3275–3305
Rani R, Lobiyal D K (2021) A weighted word embedding based approach for extractive text summarization. Exp Syst Applic, 186. https://doi.org/10.1016/j.eswa.2021.115867
Rush A M, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization. Computer Science
See A, Liu P J, Manning C D (2017) Get to the point: summarization with pointer-generator networks. In: Barzilay R, Kan MY (eds) Proceedings of the 55th annual mtting of the association for computation linguistics (ACL 2017), vol 1. Assoc Computational Linguistics-Acl, Stroudsburg, pp 1073–1083
Shaojie Bai V K (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling
Song S, Huang H, Ruan T (2019) Abstractive text summarization using lstm-cnn based deep learning. Multimed Tools Applic 78(1):857–875. https://doi.org/10.1007/s11042-018-5749-3
Takase S, Suzuki J, Okazaki N, Hirao T, Nagata M (2016) Neural headline generation on abstract meaning representation. In: Proceedings of the 2016 conference on empirical methods in natural language processing
Wang B (2018) Disconnected recurrent neural networks for text categorization. In: Gurevych I, Miyao Y (eds) Proceedings of the 56th annual meeting of the Association for Computional Linguistics (ACL), vol 1. Assoc Computational Linguistics-ACL, Stroudsburg, pp 2311–2320
Wang W, Chang B (2016) Graph-based dependency parsing with bidirectional lstm. In: Proceedings of the 54th annual meeting of the association for computational linguistics (volume 1: long papers)
Wang Q, Ren J (2021) Summary-aware attention for social media short text abstractive summarization. Neurocomputing 425:290–299. https://doi.org/10.1016/j.neucom.2020.04.136
Wang P, Chen P, Yuan Y, Liu D, Huang Z, Hou X, Cottrell G (2018) Understanding convolution for semantic segmentation. In: 2018 IEEE Winter Conference on Applications of Computer Vision (WACV 2018). IEEE, New York, pp 1451–1460
Wang K, Quan X, Wang R (2019) Biset: Bi-directional selective encoding with template for abstractive summarization. In: Korhonen A, Traum D, Marquez L (eds) 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019). Assoc Computational Linguistics-ACL, Stroudsburg, pp 2153–2162
Wang L, Yang M, Li C, Shen Y, Xu R (2021) Abstractive text summarization with hierarchical multi-scale abstraction modeling and dynamic memory. Assoc Computing Machinery, New York, pp 2086–2090
Xiao W, Carenini G (2019) Extractive summarization of long documents by combining global and local context
Xu W, Li C, Lee M, Zhang C (2020) Multi-task learning for abstractive text summarization with key information guide network. EURASIP J Adv Signal Process 2020:1. https://doi.org/10.1186/s13634-020-00674-7
Yadav V, Bethard S (2019) A survey on recent advances in named entity recognition from deep learning models
Yao K, Zhang L, Du D, Luo T, Tao L, Wu Y (2020) Dual encoding for abstractive text summarization. IEEE Trans Cybern 50:985–996. https://doi.org/10.1109/TCYB.2018.2876317
Yu F, Koltun V (2016) Multi-scale context aggregation by dilated convolutions
Zhang Z, Wang X, Jung C (2019) Dcsr: dconvolutions for single image super-resolution. IEEE Trans Image Proceed 28:1625–1635. https://doi.org/10.1109/TIP.2018.2877483
Zhou Q, Yang N, Wei F, Zhou M (2017) Selective encoding for abstractive sentence summarization. In: Barzilay R, Kan MY (eds) Proceedings of the 55th annual meeting of the assocaition for computational linguistics (ACL 2017), vol 1. Assoc Computational Linguistics-ACL, Stroudsburg, pp 1095–1104
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant Nos. 61802107); Handan science and technology R & D plan project(Grant Nos. 21422093285); Hebei Natural Science Foundation (Youth) project (Grant Nos. D2021402043).
Funding
Funding This work was supported by the National Natural Science Foundation of China (Grant Nos. 61802107); Handan science and technology R & D plan project(Grant Nos. 21422093285); Hebei Natural Science Foundation (Youth) project (Grant Nos. D2021402043).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
The authors have no relevant financial or non-financial interests to disclose.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Yuan Huang, Weijian Huang and Wei Wang contributed equally to this work.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Li, Y., Huang, Y., Huang, W. et al. A global and local information extraction model incorporating selection mechanism for abstractive text summarization. Multimed Tools Appl 83, 4859–4886 (2024). https://doi.org/10.1007/s11042-023-15274-4
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-15274-4