@inproceedings{chowdhury-ferdous-2025-boigenre,
    title = "{BOIGENRE}: A Large-Scale {B}angla Dataset for Genre Classification from Book Summaries",
    author = "Chowdhury, Rafi Hassan  and
      Ferdous, Rahanuma Ryaan",
    editor = "Alam, Firoj  and
      Kar, Sudipta  and
      Chowdhury, Shammur Absar  and
      Hassan, Naeemul  and
      Prince, Enamul Hoque  and
      Tasnim, Mohiuddin  and
      Rony, Md Rashad Al Hasan  and
      Rahman, Md Tahmid Rahman",
    booktitle = "Proceedings of the Second Workshop on Bangla Language Processing (BLP-2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/old-master/2025.banglalp-1.20/",
    pages = "249--258",
    ISBN = "979-8-89176-314-2",
    abstract = "The classification of literary genres plays a vital role in digital humanities and natural language processing (NLP), supporting tasks such as content organization, recommendation, and linguistic analysis. However, progress for the Bangla language remains limited due to the lack of large, structured datasets. To address this gap, we present BOIGENRE, the first large-scale dataset for Bangla book genre classification, built from publicly available summaries. The dataset contains 25,951 unique samples across 16 genres, showcasing diversity in narrative style, vocabulary, and linguistic expression. We provide statistical insights into text length, lexical richness, and cross-genre vocabulary overlap. To establish benchmarks, we evaluate traditional machine learning, neural, and transformer-based models. Results show that while unigram-based classifiers perform reasonably, transformer models, particularly BanglaBERT, achieve the highest F1-score of 69.62{\%}. By releasing BOIGENRE and baseline results, we offer a valuable resource and foundation for future research in Bangla text classification and low-resource NLP."
}