A Comprehensive Survey on Abstractive Text Summarization with a Focus on Low Resource Languages and Challenges in Kannada Language

R, Puneeth and Somasekar, J. (2026) A Comprehensive Survey on Abstractive Text Summarization with a Focus on Low Resource Languages and Challenges in Kannada Language. Journal of Innovation and Technology, 2026 (16). 139 -150. ISSN 2805-5179

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Official URL: http://ipublishing.intimal.edu.my/joint.html

Abstract

The exponential growth of digital textual data has made automated text summarization a critical technology. Abstractive summarization, which generates novel human-like summaries using transformer-based models such as BERT, BART, and T5, has achieved strong results on high-resource languages, yet low-resource languages like Kannada remain severely underexplored. This survey reviews abstractive summarization methodologies from early neural architectures to advanced transformer frameworks, with special attention to knowledge-augmented models and multilingual pretraining. We critically examine challenges in Indic language summarization — including data scarcity, factual inconsistency, and evaluation limitations — and identify concrete research directions for Kannada: benchmark dataset creation, culturally informed modeling, cross-lingual transfer learning, and knowledge-aware summarization pipelines

Item Type:	Article
Uncontrolled Keywords:	Abstractive Text Summarization, Low-Resource Languages, Kannada NLP, Transformer Models, Multilingual Models, Data Scarcity.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	22 Jun 2026 06:24
Last Modified:	22 Jun 2026 06:24
URI:	http://eprints.intimal.edu.my/id/eprint/2334

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