Abstract
This paper proposes a novel methodology to generate an extractive text summary from a corpus of documents. Unlike most existing methods, our approach is designed in such a way that the final generated summary covers all the important topics from a corpus of documents. We propose a heuristic method which uses the Latent Dirichlet Allocation technique to identify the optimum number of independent topics present in the corpus. Some of the sentences are identified as the important sentences from each independent topic using a set of word and sentence level features. In order to ensure that the final summary is coherent, we suggest a novel technique to reorder the sentences based on sentence similarity. The use of topic modeling ensures that all the important content from the corpus of documents is captured in the extracted summary which in turn strengthen the summary. Experimental results show that the proposed approach is promising.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Since reduction is being performed, \(2/X<1\).
- 2.
Experimental results generate a good summary for X = 13.
- 3.
- 4.
By independent means low similarity between topics.
- 5.
- 6.
- 7.
References
Miller, G.A.: Wordnet: a lexical database for English. Commun. ACM 38(11), 39–41 (1995)
Ganesan, K., Zhai, C., Han, J.: Opinosis: a graph-based approach to abstractive summarization of highly redundant opinions. In: Proceedings of the 23rd International Conference on Computational Linguistics, pp. 340–348. Association for Computational Linguistics (2010)
Moratanch, N., Chitrakala, S.: A survey on extractive text summarization. In: 2017 International Conference on Computer, Communication and Signal Processing (ICCCSP), pp. 1–6. IEEE (2017)
Fang, C., Mu, D., Deng, Z., Wu, Z.: Word-sentence co-ranking for automatic extractive text summarization. Expert Syst. Appl. 72, 189–195 (2017)
Nallapati, R., Zhai, F., Zhou, B.: SummaRuNNer: a recurrent neural network based sequence model for extractive summarization of documents. In: AAAI, pp. 3075–3081 (2017)
Roul, R.K., Sahoo, J.K., Goel, R.: Deep learning in the domain of multi-document text summarization. In: Shankar, B.U., Ghosh, K., Mandal, D.P., Ray, S.S., Zhang, D., Pal, S.K. (eds.) PReMI 2017. LNCS, vol. 10597, pp. 575–581. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69900-4_73
Narayan, S., Cohen, S.B., Lapata, M.: Ranking sentences for extractive summarization with reinforcement learning. In: 16th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. US. ACL anthology, New Orleans (2018)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)
Kullback, S., Leibler, R.A.: On information and sufficiency. Ann. Math. Stat. 22(1), 79–86 (1951)
Fuglede, B., Topsoe, F.: Jensen-Shannon divergence and Hilbert space embedding. In: Proceedings, International Symposium on Information Theory. ISIT 2004, p. 31. IEEE (2004)
Lin, C.-Y.: Rouge: a package for automatic evaluation of summaries. In: Text Summarization Branches Out: Proceedings of the ACL-04 Workshop, vol. 8, pp. 74–81 (2004)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Roul, R.K., Mehrotra, S., Pungaliya, Y., Sahoo, J.K. (2019). A New Automatic Multi-document Text Summarization using Topic Modeling. In: Fahrnberger, G., Gopinathan, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2019. Lecture Notes in Computer Science(), vol 11319. Springer, Cham. https://doi.org/10.1007/978-3-030-05366-6_17
Download citation
DOI: https://doi.org/10.1007/978-3-030-05366-6_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-05365-9
Online ISBN: 978-3-030-05366-6
eBook Packages: Computer ScienceComputer Science (R0)