BanglaSense: Bangla Text Summarization Using Generative AI with Large Language Model

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dc.contributor.advisorDr. Shahnewaz Siddique
dc.contributor.authorMd. Jahidul Islam
dc.contributor.id1831533042
dc.coverage.departmentElectrical and Computer Engineering
dc.date.accessioned2025
dc.date.accessioned2025-07-09T09:34:48Z
dc.date.available2025-07-09T09:34:48Z
dc.date.issued2023
dc.description.abstractText summarization is used in various domains, such as generating concise summaries of news articles, documents including research papers, social media’s long-form posts, and conversations to quickly and easily understand a long text. On abstractive text summarization, due to the scarcity of datasets and computing resources for mid-level languages, recent efforts have concentrated mostly on high-resource languages like English. In this research, we propose a Bangla Text Summarization system Using Generative AI with a Large Language Model. We use in-context learning techniques and instruction fine-tuning to train the mT5 pre-trained multilingual Transformer model on the XL-Sum (Bangla) dataset from the Hugging Face library. Our fine-tuned model performed the best in terms of relevancy and correctness with a remarkable 45.41 rouge1, and 33.85 regulome Scores.
dc.description.degreeUndergraduate
dc.identifier.cd600000174
dc.identifier.print-thesisTo be assigned
dc.identifier.urihttps://repository.northsouth.edu/handle/123456789/1235
dc.language.isoen
dc.publisherNorth South University
dc.rights©NSU Library
dc.titleBanglaSense: Bangla Text Summarization Using Generative AI with Large Language Model
oaire.citation.endPage25
oaire.citation.startPage1
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