Comparative Analysis of Lexical Chain, Bidirectional Encoder Representations from Transformers (BERT) and Graph based Approaches for Condensation of Low Resource Language Documents

Authors

  • Pranjali Deshpande Department of Computer Engineering, MKSSS’s Cummins College of Engg. for Women, Pune-52, India
  • Sunita Jahirabadkar Department of Computer and Information Technology (CI), Department of Technology, SPPU, Pune-07, India

Keywords:

Automatic Summarization, BERT, Extractive Summarization, Graph based approach, Low Resource Languages, Lexical Chain

Abstract

Humans use language as their primary and exclusive means of communication. There are around 7000 different languages spoken in the world. Among them, Low Resource Languages (LRL) are ones that do not have the linguistic resources required to create statistical NLP applications. The most common way that people express and store their thoughts is through writing. Technological developments are making the world smaller by making distant communication more accessible. Owing to the rise in internet usage, fresh textual content is created every second. Not all of the information in this text is helpful. In light of this, document condensation or summarization is becoming a more important responsibility. There are two methods for creating summaries: extractive and abstractive. While essential phrases and sentences from the original document are kept in an extractive summary, an abstractive summary is created by reworking the main sentences. When it comes to LRL materials, summarizing becomes more difficult. The studies for condensation or automatic summarization of LRL documents using BERT, lexical chain and Graph based approach are the main topic of this study.

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References

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Published

23.02.2024

How to Cite

Deshpande, P. ., & Jahirabadkar, S. . (2024). Comparative Analysis of Lexical Chain, Bidirectional Encoder Representations from Transformers (BERT) and Graph based Approaches for Condensation of Low Resource Language Documents. International Journal of Intelligent Systems and Applications in Engineering, 12(16s), 08–15. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4777

Issue

Vol. 12 No. 16s (2024)

Section

Research Article

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