Skip to main content
SpringerLink
Log in

Lattice abstraction-based content summarization using baseline abstractive lexical chaining progress

  • Original Research
  • Published:
International Journal of Information Technology Aims and scope Submit manuscript

Abstract

Text summarization is essential in this fast-growing world to read the information because a vast amount of information holds various definitions among related contents. Due to this, reading loads of information documents becomes more tedious. Most text summarization techniques are based on information extraction from unstructured documents, leading to more non-residual abstraction in sentence case analysis. To resolve this problem, a Lattice abstraction-based content summarization (Labs-CS) is proposed to reduce the unstructured documents using the Intra sub-cluster to precipitate sentences. Initially, this proposed method preprocesses natural language processing with a dictionary of terms to make corpus reader content analysis and then de-noises the contents by eliminating the nonstructural text in segmented sentences. Depending on the structural segmentation, the key terms are grouped into clusters and summarized in the sentences into intra-cluster comparisons in another cluster. It creates a lattice-based essential term fragmentation; the text terms are splatted into residual and non-residual terms, then the residual terms are compared with a dictionary of syntactic words which are extracted. Based on the extracted terms, Baseline Abstractive Sentences (BAS) are created using Lexical Chaining Progress (LCP). Finally, the syntactic sequence analyzer combines the extracted term to summarize a document. The proposed system produces high performance by achieving high coherence to reduce the complexity of summarized multilingual documents.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Rezaei S, Dami, Daneshjoo P (2019) Multi-document extractive text summarization via deep learning approach. In: 2019 5th conference on knowledge-based engineering and innovation (KBEI), pp 680–685. https://doi.org/10.1109/KBEI.2019.8735084

  2. Jindal SG, Kaur A (2020) Automatic Keyword and Sentence-Based Text Summarization for Software Bug Reports. In: IEEE Access, vol 8, pp 65352–65370.  https://doi.org/10.1109/ACCESS.2020.2985222

  3. Sun X, Zhuge H (2018) Summarization of Scientific Paper Through Reinforcement Ranking on Semantic Link Network. In: IEEE Access, vol 6, pp 40611–40625. https://doi.org/10.1109/ACCESS.2018.2856530

  4. Mridha MF, Lima AA, Nur K, Das SC, Hasan M, Kabir MM (2021) A Survey of Automatic Text Summarization: Progress, Process and Challenges. In: IEEE Access, vol 9, pp 156043–156070. https://doi.org/10.1109/ACCESS.2021.3129786

  5. Jang H, Kim W (2021) Reinforced Abstractive Text Summarization With Semantic Added Reward. In: IEEE Access, vol 9, pp 103804–103810. https://doi.org/10.1109/ACCESS.2021.3097087

  6. Lotfian R, Busso C (2019) Lexical Dependent Emotion Detection Using Synthetic Speech Reference. In: IEEE Access, vol 7, pp 22071–22085. https://doi.org/10.1109/ACCESS.2019.2898353

  7. Friedrich M, Friederici AD (2005) Phonotactic knowledge and lexical-semantic processing in one-year-olds: brain responses to words and nonsense words in picture contexts. J Cogn Neurosci 17(11):1785–1802. https://doi.org/10.1162/089892905774589172

  8. Sarwar TB, Noor NM, Miah MSU, Rashid M, Farid FA, Husen MN (2021) Recommending Research Articles: A Multi-Level Chronological Learning-Based Approach Using Unsupervised Keyphrase Extraction and Lexical Similarity Calculation. In: IEEE Access, vol 9, pp 160797–160811. https://doi.org/10.1109/ACCESS.2021.3131470

  9. Cheng J, Zhang F, Guo X, Syntax-Augmented A (2020) and Headline-Aware Neural Text Summarization Method. In: IEEE Access, vol 8, pp 218360–218371. https://doi.org/10.1109/ACCESS.2020.3042886

  10. Syed A, Gaol FL, Matsuo T (2021) A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization. In: IEEE Access, vol 9, pp 13248–13265. https://doi.org/10.1109/ACCESS.2021.3052783

  11. Saeed MY, Awais M, Talib R, Younas M (2020) Unstructured Text Documents Summarization With Multi-Stage Clustering. In: IEEE Access, vol 8, pp 212838–212854. https://doi.org/10.1109/ACCESS.2020.3040506

  12. Yao K, Zhang L, Du D, Luo T, Tao L, Wu Y (2020) Dual Encoding for Abstractive Text Summarization. IEEE Trans Cybern 50(3):985–996. https://doi.org/10.1109/TCYB.2018.2876317

  13. Du Y, Huo H (2020) News Text Summarization Based on Multi-Feature and Fuzzy Logic. IEEE Access 8:140261–140272. https://doi.org/10.1109/ACCESS.2020.3007763

    Article  Google Scholar 

  14. Hernández-Castañeda Á, García-Hernández RA, Ledeneva Y, Millán-Hernández CE (2020) “Extractive Automatic Text Summarization Based on Lexical-Semantic Keywords. In: IEEE Access, vol 8, pp 49896–49907. https://doi.org/10.1109/ACCESS.2020.2980226

  15. Alqaisi R, Ghanem W, Qaroush A (2020) Extractive Multi-Document Arabic Text Summarization Using Evolutionary Multi-Objective Optimization With K-Medoid Clustering. In: IEEE Access, vol 8, pp 228206–228224. https://doi.org/10.1109/ACCESS.2020.3046494

  16. Sanchez-Gomez JM, Vega-Rodríguez MA, Pérez CJ (2019) An Indicator-based Multi-Objective Optimization Approach Applied to Extractive Multi-Document Text Summarization. IEEE Lat Am Trans 17(08):1291–1299. https://doi.org/10.1109/TLA.2019.8932338

  17. You F, Zhao S, Chen J (2020) A Topic Information Fusion and Semantic Relevance for Text Summarization. In: IEEE Access, vol 8, pp 178946–178953. https://doi.org/10.1109/ACCESS.2020.2999665

  18. Gambhir M, Gupta V (2017) Recent automatic text summarization techniques: a survey. Artif Intell Rev 47:1–66. https://doi.org/10.1007/s10462-016-9475-9

    Article  Google Scholar 

  19. Ghosh S (2021) Identifying click baits using various machine learning and deep learning techniques. Int J Inf Tecnol 13:1235–1242. https://doi.org/10.1007/s41870-020-00473-1

    Article  Google Scholar 

  20. Amin HMA, Arefin MS, Dhar PK (2020) A method for video categorization by analyzing text, audio, and frames. Int J Inf Tecnol 12:889–898. https://doi.org/10.1007/s41870-019-00338-2

    Article  Google Scholar 

  21. Su M-H, Wu C-H, Cheng H-T (2020) A Two-Stage Transformer-Based Approach for Variable-Length Abstractive Summarization. In: IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol 28, pp 2061–2072. https://doi.org/10.1109/TASLP.2020.3006731

  22. Barman D, Chowdhury N (2020) A novel semi-supervised approach for text classification. Int J Inf Tecnol 12:1147–1157. https://doi.org/10.1007/s41870-018-0137-9

    Article  Google Scholar 

  23. Jang M, Kang P (2021) Learning-Free Unsupervised Extractive Summarization Model. In: IEEE Access, vol 9, pp 14358–14368. https://doi.org/10.1109/ACCESS.2021.3051237

  24. Guo Q, Huang J, Xiong N, Wang P, Network MS-Pointer (2019) Abstractive Text Summary Based on Multi-Head Self-Attention. In: IEEE Access, vol 7, pp 138603–138613. https://doi.org/10.1109/ACCESS.2019.2941964

  25. Alshanqiti A, Namoun A, Alsughayyir A, Mashraqi AM, Gill AR, Albouq SS (2021) Leveraging DistilBERT for Summarizing Arabic Text: An Extractive Dual-Stage Approach,“ in IEEE Access, vol. 9, pp. 135594–135607, DOI: https://doi.org/10.1109/ACCESS.2021.3113256

  26. Bichi A, Samsudin R, Hassan R, Almekhlafi K (2021) A review of graph-based extractive text summarization models. https://doi.org/10.1007/978-3-030-70713-2_41

  27. Aruda O, Rini dian P, Yusliani N (2020) Multi-Document Text Summarization Based on Semantic Clustering and Selection of Representative Sentences Using Latent Dirichlet Allocation. https://doi.org/10.2991/aisr.k.200424.029

  28. Shini R, Subha (2021) Recurrent Neural Network based Text Summarization Techniques by Word Sequence Generation. 1224–1229. https://doi.org/10.1109/ICICT50816.2021.9358764. Ambeth Kumar

  29. Prasanna Kumar R, Bharathi G, Mohan (2021) A Comprehensive Survey on Topic Modeling in Text Summarization. In: 5th International Conference on Micro-Electronics and Telecommunication Engineering, Springer book series on “Lecture Notes in Networks and System

  30. Yamuna K, Shriamrut V, Singh D, Gopalasamy V, Menon V, Technologies N (2021) (ICCCNT), pp 1–6. https://doi.org/10.1109/ICCCNT51525.2021.9579748

  31. Raj D, Geetha M (2018) A Trigraph Based Centrality Approach Towards Text Summarization. In: 2018 International Conference on Communication and Signal Processing (ICCSP), Chennai, India,

  32. Bharathi Mohan G, Kumar RP (2022) Survey of Text Document Summarization based on Ensemble Topic Vector Clustering Model. In: 4th International conference on Computer Networks, Big Data and IoT, Springer Book Series on Lecture Notes on Data Engineering and Communication Technologies

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India

    G. Bharathi Mohan & R. Prasanna Kumar

Authors
  1. G. Bharathi Mohan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. R. Prasanna Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to G. Bharathi Mohan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mohan, G.B., Kumar, R.P. Lattice abstraction-based content summarization using baseline abstractive lexical chaining progress. Int. j. inf. tecnol. 15, 369–378 (2023). https://doi.org/10.1007/s41870-022-01080-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41870-022-01080-y

Keywords

Search

Navigation