Multimedia Tools and Applications

, Volume 78, Issue 1, pp 857–875 | Cite as

Abstractive text summarization using LSTM-CNN based deep learning

  • Shengli SongEmail author
  • Haitao Huang
  • Tongxiao Ruan


Abstractive Text Summarization (ATS), which is the task of constructing summary sentences by merging facts from different source sentences and condensing them into a shorter representation while preserving information content and overall meaning. It is very difficult and time consuming for human beings to manually summarize large documents of text. In this paper, we propose an LSTM-CNN based ATS framework (ATSDL) that can construct new sentences by exploring more fine-grained fragments than sentences, namely, semantic phrases. Different from existing abstraction based approaches, ATSDL is composed of two main stages, the first of which extracts phrases from source sentences and the second generates text summaries using deep learning. Experimental results on the datasets CNN and DailyMail show that our ATSDL framework outperforms the state-of-the-art models in terms of both semantics and syntactic structure, and achieves competitive results on manual linguistic quality evaluation.


Text mining Abstractive text summarization Relation extraction Deep learning 


  1. 1.
    Angeli G, Tibshirani J, Wu J et al (2014) Combining distant and partial supervision for relation extraction[C]. EMNLP, pp 1556–1567Google Scholar
  2. 2.
    Bing L, Li P, Liao Y et al (2015) Abstractive multi-document summarization via phrase selection and merging[J]. arXiv preprint arXiv:1506.01597Google Scholar
  3. 3.
    Cao Z, Li W, Li S et al (2016) Attsum: joint learning of focusing and summarization with neural attention[J]. arXiv preprint arXiv:1604.00125Google Scholar
  4. 4.
    Chen M, Weinberger KQ, Sha F (2013) An alternative text representation to TF-IDF and Bag-of-Words[J]. arXiv preprint arXiv:1301.6770Google Scholar
  5. 5.
    Cheng J, Lapata M (2016) Neural summarization by extracting sentences and words[J]. arXiv preprint arXiv:1603.07252Google Scholar
  6. 6.
    Chopra S, Auli M, Rush AM (2016) Abstractive sentence summarization with attentive recurrent neural networks[C]. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp 93–98Google Scholar
  7. 7.
    Colmenares CA, Litvak M, Mantrach A et al (2015) HEADS: headline generation as sequence prediction using an abstract feature-rich space[C]. HLT-NAACL, pp 133–142Google Scholar
  8. 8.
    Erkan G, Radev DR (2004) Lexrank: graph-based lexical centrality as salience in text summarization[J]. J Artif Intell Res 22:457–479CrossRefGoogle Scholar
  9. 9.
    Filippova K, Altun Y (2013) Overcoming the lack of parallel data in sentence compression[C]. EMNLP, pp 1481–1491Google Scholar
  10. 10.
    Gu J, Lu Z, Li H et al (2016) Incorporating copying mechanism in sequence-to-sequence learning[J]. arXiv preprint arXiv:1603.06393Google Scholar
  11. 11.
    Hu B, Chen Q, Zhu F (2015) Lcsts: a large scale chinese short text summarization dataset[J]. arXiv preprint arXiv:1506.05865Google Scholar
  12. 12.
    Li J, Luong MT, Jurafsky D (2015) A hierarchical neural autoencoder for paragraphs and documents[J]. arXiv preprint arXiv:1506.01057Google Scholar
  13. 13.
    Lin CY, Hovy E (2003) Automatic evaluation of summaries using n-gram co-occurrence statistics[C]. Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology-Volume 1. Association for Computational Linguistics, pp 71–78Google Scholar
  14. 14.
    Litvak M, Last M (2008) Graph-based keyword extraction for single-document summarization[C]. Proceedings of the workshop on Multi-source Multilingual Information Extraction and Summarization. Association for Computational Linguistics, pp 17–24Google Scholar
  15. 15.
    Lopyrev K (2015) Generating news headlines with recurrent neural networks[J]. arXiv preprint arXiv:1512.01712Google Scholar
  16. 16.
    Nallapati R, Zhou B, Gulcehre C et al (2016) Abstractive text summarization using sequence-to-sequence rnns and beyond[J]. arXiv preprint arXiv:1602.06023Google Scholar
  17. 17.
    Nallapati R, Zhai F, Zhou B (2017) Summarunner: a recurrent neural network based sequence model for extractive summarization of documents. In: Proc. Thirty-First AAAI Conference on Artificial Intelligence, pp 3075–3081Google Scholar
  18. 18.
    Ribeiro R, Marujo L, Martins de Matos D et al (2013) Self reinforcement for important passage retrieval[C]. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, pp 845–848Google Scholar
  19. 19.
    Riedhammer K, Favre B, Hakkani-Tür D (2010) Long story short–global unsupervised models for keyphrase based meeting summarization[J]. Speech Comm 52(10):801–815CrossRefGoogle Scholar
  20. 20.
    Rush AM, Chopra S, Weston J (2015) A neural attention model for abstractive sentence summarization[J]. arXiv preprint arXiv:1509.00685Google Scholar
  21. 21.
    Sarkar K (2012) Bengali text summarization by sentence extraction[J]. arXiv preprint arXiv:1201.2240Google Scholar
  22. 22.
    Wong KF, Wu M, Li W (2008) Extractive summarization using supervised and semi-supervised learning[C]. Proceedings of the 22nd International Conference on Computational Linguistics-Volume 1. Association for Computational Linguistics, pp 985–992Google Scholar
  23. 23.
    Yousefi-Azar M, Text HL (2017) summarization using unsupervised deep learning[J]. Expert Syst Appl 68:93–105CrossRefGoogle Scholar
  24. 24.
    Zhang Y, Shen D, Wang G et al (2017) Deconvolutional paragraph representation learning[C]. Advances in Neural Information Processing Systems, pp 4172–4182Google Scholar
  25. 25.
    Zhou Q (2016) Research on heterogeneous data integration model of group enterprise based on cluster computing[J]. Clust Comput 19(3):1275–1282. CrossRefGoogle Scholar
  26. 26.
    Zhou Q (2018) Multi-layer affective computing model based on emotional psychology[J]. Electron Commer Res 18(1):109–124. CrossRefGoogle Scholar
  27. 27.
    Zhou Q, Liu R (2016) Strategy optimization of resource scheduling based on cluster rendering[J]. Clust Comput 19(4):2109–2117. MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Software Engineering InstituteXidian UniversityXi’anChina
  2. 2.School of Computer Science and TechnologyXidian UniversityXi’anChina

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