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http://dx.doi.org/10.3745/KTSDE.2019.8.8.343

Single Document Extractive Summarization Based on Deep Neural Networks Using Linguistic Analysis Features  

Lee, Gyoung Ho (충남대학교 전자전파정보통신공학과)
Lee, Kong Joo (충남대학교 전파정보통신공학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.8, no.8, 2019 , pp. 343-348 More about this Journal
Abstract
In recent years, extractive summarization systems based on end-to-end deep learning models have become popular. These systems do not require human-crafted features and adopt data-driven approaches. However, previous related studies have shown that linguistic analysis features such as part-of-speeches, named entities and word's frequencies are useful for extracting important sentences from a document to generate a summary. In this paper, we propose an extractive summarization system based on deep neural networks using conventional linguistic analysis features. In order to prove the usefulness of the linguistic analysis features, we compare the models with and without those features. The experimental results show that the model with the linguistic analysis features improves the Rouge-2 F1 score by 0.5 points compared to the model without those features.
Keywords
Single Document Summarization; Extractive Summarization; Linguistic Analysis Features; Deep Neural Networks;
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1 R. Nallapati, et al., Abstractive textsummarization using sequence-to-sequence rnns and beyond. arXiv preprintarXiv: 1602.06023, 2016.
2 R. Nallapati, F. Zhai, and B. Zhou, "Summarunner: A recurrent neural network based sequence model for extractive summarization ofdocuments," in Thirty-First AAAI Conference on Artificial Intelligence, 2017.
3 A. Jadhav and V. Rajan, "Extractive Summarizationwith SWAP-NET: Sentences and Words from Alternating Pointer Networks," in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2018.
4 A. Nenkova, and L. Vanderwende, "The impact offrequency on summarization," Microsoft Research, Redmond, Washington, Tech. Rep.MSR-TR-2005, 2005. 101.
5 E. Filatova and V. Hatzivassiloglou, "Event-based extractive summarization," 2004.
6 G. Erkan, and D. R. Radev, "Lexrank: Graph-basedlexical centrality as salience in text summarization," Journal of Artificial Intelligence Research, Vol.22, pp.457-479, 2004.   DOI
7 D. R. Radev, et al., "Centroid-based summarizationof multiple documents," Information Processing & Management, Vol.40, No.6, pp.919-938, 2004.   DOI
8 R. McDonald, "A study of global inferencealgorithms in multi-document summarization," in European Conference onInformation Retrieval, 2007. Springer.
9 D. Shen, et al., "Document summarization usingconditional random fields," IJCAI, Vol.7, pp.2862-2867, 2007.
10 H. P. Edmundson, "New methods in automaticextracting," Journal of the ACM (JACM), Vol.16, No.2, pp.264-285, 1969.   DOI
11 I. Sutskever, O. Vinyals, and Q. V. Le, "Sequence to sequence learning with neural networks," in Advances in Neural Information Processing Systems, 2014.
12 J. Cheng and M. Lapata, "Neural summarization byextracting sentences and words," arXiv preprint arXiv:1603.07252, 2016.
13 K. Cho, et al., "Learning phrase representationsusing RNN encoder-decoder for statistical machine translation," arXiv preprintarXiv:1406.1078, 2014.
14 Y. Bengio, et al., "A neural probabilisticlanguage model," Journal of Machine Learning Research, Vol.3(Feb), pp.1137-1155, 2003.
15 T. Mikolov, et al., "Efficient estimation of wordrepresentations in vector space," arXiv preprint arXiv:1301.3781, 2013.
16 S. Menaka and N. Radha, "Text classificationusing keyword extraction technique," International Journal of Advanced Researchin Computer Science and Software Engineering, Vol.3, No.12, 2013.
17 A. Hulth, "Improved automatic keyword extractiongiven more linguistic knowledge," in Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, 2003. Association for Computational Linguistics.
18 M. Wu, et al., "Event-based summarization usingtime features," in International Conference on Intelligent Text Processing and Computational Linguistics, 2007. Springer.
19 W. Li, et al., "Extractive summarization usinginter-and intra-event relevance," in Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of theAssociation for Computational Linguistics, 2006. Association for Computational Linguistics.
20 K. M. Hermann, et al., "Teaching machines to readand comprehend," in Advances in Neural Information Processing Systems, 2015.
21 C. Manning, et al., "The Stanford CoreNLP naturallanguage processing toolkit," in Proceedings of 52nd annual meeting of theassociation for computational linguistics: system demonstrations, 2014.
22 D. P. Kingma and J. Ba, "Adam: A method forstochastic optimization," arXiv preprint arXiv:1412.6980, 2014.
23 C.-Y. Lin, "Rouge: A package for automaticevaluation of summaries," Text Summarization Branches Out, pp.74-81, 2004.