{
  "abstract": "This work aims to develop a domain-adapted Bengali text summarization model by training and fine-tuning on general and domain-specific datasets with categories such as state, international, and sports. Flan-T5 and mT5 LLMs were trained on the XLSUM Bengali dataset for text summarization, and they were trained on source domains and fine-tuned on the target domains for domain adaptation. In general-domain summarization on the XLSum Bengali dataset, mT5 achieved stronger overall performance than Flan-T5, with ROUGE-1 and BERTScore values of 0.21 and 0.72, respectively. The Flan-T5-XLSUM model outperformed other LLMs, achieving a ROUGE score of 0.79, a BLEU score of 0.13 and a BERTScore of 0.86. A human evaluation involving 28 participants was conducted to assess summary fluency, adequacy, and factual consistency across domains, confirming the qualitative effectiveness of the proposed models. A custom dataset is developed with manually annotated categories for domain adaptation purposes, contributing to domain-specific Bengali data. The ablation study illustrated that pre-training and fine-tuning on domain-specific data significantly enhanced model performance, with Flan-T5 fine-tuned on sports data. Three explainability methods (LIME, SHAP, and BertViz) revealed that geographically and contextually meaningful tokens strongly influenced the domain-adaptive Flan-T5 Bengali summarization model. The adapter-based Flan-T5 architecture enables effective multi-domain fine-tuning with less than 1% of trainable parameters while preserving model performance.",
  "abstract_shown": true,
  "affiliations": [
    "Electrical and Computer Engineering, North South University, Dhaka, Bangladesh"
  ],
  "article_type": "Research Article",
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  "author_total": 6,
  "authors": [
    {
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        0
      ],
      "name": "Anusree Roy"
    },
    {
      "name": "Fahrin Hossain Sunaira"
    },
    {
      "name": "Tateyama Orpa"
    },
    {
      "name": "Farasha Shamma Yussouf"
    },
    {
      "name": "Intisar Tahmid Naheen"
    },
    {
      "name": "Riasat Khan"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/900jjwhykjfbm.bib",
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  "doi": "10.1371/journal.pone.0357890",
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  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
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  "native_subjects": [
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      "openalex",
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    ],
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      "plos",
      "/Biology and life sciences/Neuroscience/Cognitive science/Cognitive psychology/Language"
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      "/Engineering and technology/Software engineering/Programming languages"
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      "/People and places/Population groupings/Ethnicities/Asian people/Bengali people"
    ],
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      "/Social sciences/Linguistics/Semantics"
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      "/Social sciences/Psychology/Cognitive psychology/Language"
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      "/Social sciences/Psychology/Cognitive psychology/Language/Multilingualism"
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  "openalex": "W7220367584",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0357890&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/900jjwhykjfbm.ris",
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    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0357890",
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      "url": "https://doi.org/10.1371/journal.pone.0357890"
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  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
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  "title": "Domain-adaptive Bengali text summarization: Harnessing transformer-based large language models in closed-set contexts",
  "title_text": "Domain-adaptive Bengali text summarization: Harnessing transformer-based large language models in closed-set contexts",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/900jjwhykjfbm",
  "venue": "PLOS One",
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