Abstract
Extractive summarization consists of generating a summary by ranking sentences from the original texts according to their importance and salience. Text representation is a fundamental process that affects the effectiveness of many text summarization methods. Distributed word vector representations have been shown to improve Natural Language Processing (NLP) tasks, especially Automatic Text Summarization (ATS). However, most of them do not consider the order and the context of the words in a sentence. This does not fully allow grasping the sentence semantics and the syntactic relationships between sentences constituents. In this paper, to overcome this problem, we propose a deep neural network model based-method for extractive single document summarization using the state-of-the-art sentence embedding models. Experiments are performed on the standard DUC2002 dataset using three sentence embedding models. The obtained results show the effectiveness of the used sentence embedding models for ATS. The overall comparison results show that our method outperforms eight well-known ATS baselines and achieves comparable results to the state-of-the-art deep learning based methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
- 2.
- 3.
- 4.
ROUGE-1.5.5 with options: -n 2 -m -u -c 95 -x -r 1000 -f A -p 0.5 -t.
- 5.
- 6.
- 7.
- 8.
- 9.
- 10.
References
Arora, S., Liang, Y., Ma, T.: A simple but tough-to-beat baseline for sentence embeddings (2016)
Cao, Z., Wei, F., Dong, L., Li, S., Zhou, M.: Ranking with recursive neural networks and its application to multi-document summarization. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Cao, Z., Wei, F., Li, S., Li, W., Zhou, M., Houfeng, W.: Learning summary prior representation for extractive summarization. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), vol. 2, pp. 829–833 (2015)
Cer, D., Yang, Y., Kong, S., Hua, N., Limtiaco, N., John, R.S., Constant, N., Guajardo-Cespedes, M., Yuan, S., Tar, C., et al.: Universal sentence encoder. arXiv preprint arXiv:1803.11175 (2018)
Cheng, J., Lapata, M.: Neural summarization by extracting sentences and words. arXiv preprint arXiv:1603.07252 (2016)
Denil, M., Demiraj, A., De Freitas, N.: Extraction of salient sentences from labelled documents. arXiv preprint arXiv:1412.6815 (2014)
Erkan, G., Radev, D.R.: LexRank: graph-based lexical centrality as salience in text summarization. J. Artif. Intell. Res. 22, 457–479 (2004)
Ethayarajh, K.: Unsupervised random walk sentence embeddings: a strong but simple baseline. In: Proceedings of The Third Workshop on Representation Learning for NLP, pp. 91–100 (2018)
Haghighi, A., Vanderwende, L.: Exploring content models for multi-document summarization. In: Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pp. 362–370. Association for Computational Linguistics (2009)
Iyyer, M., Manjunatha, V., Boyd-Graber, J., Daumé III, H.: Deep unordered composition rivals syntactic methods for text classification. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), vol. 1, pp. 1681–1691 (2015)
Jain, A., Bhatia, D., Thakur, M.K.: Extractive text summarization using word vector embedding. In: 2017 International Conference on Machine Learning and Data Science (MLDS), pp. 51–55. IEEE (2017)
Kågebäck, M., Mogren, O., Tahmasebi, N., Dubhashi, D.: Extractive summarization using continuous vector space models. In: Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC), pp. 31–39 (2014)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)
Kiros, R., Zhu, Y., Salakhutdinov, R.R., Zemel, R., Urtasun, R., Torralba, A., Fidler, S.: Skip-thought vectors. In: Advances in Neural Information Processing Systems, pp. 3294–3302 (2015)
Kobayashi, H., Noguchi, M., Yatsuka, T.: Summarization based on embedding distributions. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, EMNLP 2015, Lisbon, Portugal, 17–21 September 2015, pp. 1984–1989 (2015)
Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21–26 June 2014, pp. 1188–1196 (2014)
Lin, C.Y.: ROUGE: a package for automatic evaluation of summaries. In: Text Summarization Branches Out (2004)
Luhn, H.P.: The automatic creation of literature abstracts. IBM J. Res. Dev. 2(2), 159–165 (1958)
Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing (2004)
Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)
Nallapati, R., Zhai, F., Zhou, B.: Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Thirty-First AAAI Conference on Artificial Intelligence (2017)
Radev, D.R., Jing, H., Styś, M., Tam, D.: Centroid-based summarization of multiple documents. Inf. Process. Manag. 40(6), 919–938 (2004)
Ren, P., Wei, F., Zhumin, C., Jun, M., Zhou, M.: A redundancy-aware sentence regression framework for extractive summarization. In: Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 33–43 (2016)
Rossiello, G., Basile, P., Semeraro, G.: Centroid-based text summarization through compositionality of word embeddings. In: Proceedings of the MultiLing 2017 Workshop on Summarization and Summary Evaluation Across Source Types and Genres, pp. 12–21 (2017)
Saggion, H., Poibeau, T.: Automatic text summarization: past, present and future. In: Multi-Source, Multilingual Information Extraction and Summarization, pp. 3–21 (2013)
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)
Steinberger, J.: Using latent semantic analysis in text summarization and summary evaluation (2004)
Vanderwende, L., Suzuki, H., Brockett, C., Nenkova, A.: Beyond sumbasic: task-focused summarization with sentence simplification and lexical expansion. Inf. Process. Manag. 43(6), 1606–1618 (2007)
Wei, Y., Zhao, Y., Lu, C., Wei, S., Liu, L., Zhu, Z., Yan, S.: Cross-modal retrieval with cnn visual features: a new baseline. IEEE Trans. Cybern. 47(2), 449–460 (2017)
Yin, W., Pei, Y.: Optimizing sentence modeling and selection for document summarization. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (2015)
Yousefi-Azar, M., Hamey, L.: Text summarization using unsupervised deep learning. Expert Syst. Appl. 68, 93–105 (2017)
Zhang, Y., Er, M.J., Pratama, M.: Extractive document summarization based on convolutional neural networks. In: IECON 2016-42nd Annual Conference of the IEEE Industrial Electronics Society, pp. 918–922. IEEE (2016)
Zhong, S., Liu, Y., Li, B., Long, J.: Query-oriented unsupervised multi-document summarization via deep learning model. Expert Syst. Appl. 42(21), 8146–8155 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Lamsiyah, S., El Mahdaouy, A., El Alaoui, S.O., Espinasse, B. (2020). A Supervised Method for Extractive Single Document Summarization Based on Sentence Embeddings and Neural Networks. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2019). AI2SD 2019. Advances in Intelligent Systems and Computing, vol 1105. Springer, Cham. https://doi.org/10.1007/978-3-030-36674-2_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-36674-2_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-36673-5
Online ISBN: 978-3-030-36674-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)