Automatic text summarization scheme based on deep learning
ZHANG Kejun1,2, LI Weinan2, QIAN Rong1, SHI Taimeng1, JIAO Meng1
1. Department of Computer Science and Technology, Beijing Electronic Science and Technology Institute, Beijing 100070, China; 2. School of Computer Science and Technology, Xidian University, Xi'an Shaanxi 710071, China
Abstract:Aiming at the problems of inadequate semantic understanding, improper summary sentences and inaccurate summary in the field of Natural Language Processing (NLP) abstractive automatic summarization, a new automatic summary solution was proposed, including an improved word vector generation technique and an abstractive automatic summarization model. The improved word vector generation technology was based on the word vector generated by the skip-gram method. Combining with the characteristics of abstract, three word features including part of speech, word frequency and inverse text frequency were introduced, which effectively improved the understanding of words. The proposed Bi-MulRnn+ abstractive automatic summarization model was based on sequence-to-sequence (seq2seq) framework and self-encoder structure. By introducing attention mechanism, Gated Recurrent Unit (GRU) gate structure, Bi-directional Recurrent Neural Network (BiRnn) and Multi-layer Recurrent Neural Network (MultiRnn), the model improved the summary accuracy and sentence fluency of abstractive summarization. The experimental results of Large-Scale Chinese Short Text Summarization (LCSTS) dataset show that the proposed scheme can effectively solve the problem of abstractive summarization of short text, and has good performance in Rouge standard evaluation system, improving summary accuracy and sentence fluency.
张克君, 李伟男, 钱榕, 史泰猛, 焦萌. 基于深度学习的文本自动摘要方案[J]. 计算机应用, 2019, 39(2): 311-315.
ZHANG Kejun, LI Weinan, QIAN Rong, SHI Taimeng, JIAO Meng. Automatic text summarization scheme based on deep learning. Journal of Computer Applications, 2019, 39(2): 311-315.
[1] BAHDANAU D, CHO K H, BENGIO Y. Neural machine translation by jointly learning to align and translate[EB/OL].[2018-03-20]. https://arxiv.org/pdf/1409.0473v7.pdf. [2] BAHDANAU D, CHOROWSKI J, SERDYUK D, et al. End-to-end attention-based large vocabulary speech recognition[C]//Proceedings of the 2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway, NJ:IEEE, 2016:4945-4949. [3] VENUGOPALAN S, ROHRBACH M, DONAHUE J, et al. Sequence to sequence-video to text[C]//Proceedings of the 2015 IEEE International Conference on Computer Vision. Piscataway, NJ:IEEE, 2015:4534-4542. [4] RUSH A M, CHOPRA S, WESTON J. A neural attention model for abstractive sentence summarization[EB/OL].[2018-02-23]. https://arxiv.org/pdf/1509.00685.pdf. [5] CHOPRA S, AULI M, RUSH A M. Abstractive sentence summarization with attentive recurrent neural networks[EB/OL].[2018-03-21] http://aclweb.org/anthology/N/N16/N16-1012.pdf. [6] NALLAPATI R, ZHOU B W, dos SANTOS C N, et al. Abstractive text summarization using sequence-to-sequence RNNs and beyond[C]//Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning. Stroudsburg, PA:ACL, 2016:280-290. [7] ABADI M, BARHAM P, CHEN J M, et al. Tensor flow:a system for large-scale machine learning[C]//Proceedings of the 12th USENIX conference on Operating Systems Design and Implementation. Berkeley, CA:USENIX, 2016:265-283. [8] BRITZ D,GOLDIE A, LUONG M-T, et al. Massive exploration of neural machine translation architectures[EB/OL].[2018-04-05]. https://arxiv.org/pdf/1703.03906.pdf. [9] GEHRING J, AULI M, GRANGIER D, et al. Convolutional sequence to sequence learning[EB/OL].[2018-04-23]. https://arxiv.org/pdf/1705.03122.pdf. [10] LI P J, LAM W, BING L D, et al. Cascaded attention based unsupervised information distillation for compressive summarization[C]//Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:ACL, 2017:2081-2090. [11] CHUNG J Y, GULCEHRE C, CHO K H, et al. Empirical evaluation of gated recurrent neural networks on sequence modeling[EB/OL].[2018-04-23]. https://arxiv.org/pdf/1412.3555v1.pdf. [12] LOPYREV K. Generating news headlines with recurrent neural networks[EB/OL].[2018-03-20]. https://arxiv.org/pdf/1512.01712.pdf. [13] MNIH V, HEESS N, GRAVES A. Recurrent models of visual attention[EB/OL].[2018-04-08]. https://papers.nips.cc/paper/5542-recurrent-models-of-visual-attention.pdf. [14] LUONG M-T, PHAM H, MANNING C D. Effective approaches to attention-based neural machine translation[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:ACL, 2015:1412-1421. [15] JEAN S, CHO K H, MEMISEVIC R, et al. On using very large target vocabulary for neural machine translation[C]//Proceedings of the 53rd Annual Meeting of the ACL and the 7th International Joint Conference on Natural Language Processing. Stroudsburg, PA:ACL, 2015:1-10. [16] AYANA, SHEN S Q, ZHAO Y, et al. Neural headline generation with sentence-wise optimization[EB/OL].[2018-03-23]. https://arxiv.org/pdf/1604.01904.pdf. [17] LIN C Y, HOVY E. Automatic evaluation of summaries using n-gram co-occurrence statistics[C]//Proceedings of the 2003 Conference of the North American Chapter of the ACL on Human Language Technology. Stroudsburg, PA:ACL, 2003:71-78. [18] 户保田.基于深度神经网络的文本表示及其应用[D].哈尔滨:哈尔滨工业大学,2016:91-94. (HU B T. Deep neural networks for text representation and application[D]. Harbin:Harbin Institute of Technology, 2016:91-94.) [19] HU B T, CHEN Q C, ZHU F Z. LCSTS:A large scale Chinese short text summarization dataset[C]//Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Stroudsburg, PA:ACL, 2015:1967-1972.