@inproceedings{chaudhary-etal-2024-topic,
    title = "Topic Classification and Headline Generation for {M}altese Using a Public News Corpus",
    author = "Chaudhary, Amit Kumar  and
      Micallef, Kurt  and
      Borg, Claudia",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.1414",
    pages = "16274--16281",
    abstract = "The development of NLP tools for low-resource languages is impeded by the lack of data. While recent unsupervised pre-training approaches ease this requirement, the need for labelled data is crucial to progress the development of such tools. Moreover, publicly available datasets for such languages typically cover low-level syntactic tasks. In this work, we introduce new semantic datasets for Maltese generated automatically using associated metadata from a corpus in the news domain. The datasets are a news tag multi-label classification and a news abstractive summarisation task by generating its title. We also present an evaluation using publicly available models as baselines. Our results show that current models are lacking the semantic knowledge required to solve such tasks, shedding light on the need to use better modelling approaches for Maltese.",
}

The development of NLP tools for low-resource languages is impeded by the lack of data. While recent unsupervised pre-training approaches ease this requirement, the need for labelled data is crucial to progress the development of such tools. Moreover, publicly available datasets for such languages typically cover low-level syntactic tasks. In this work, we introduce new semantic datasets for Maltese generated automatically using associated metadata from a corpus in the news domain. The datasets are a news tag multi-label classification and a news abstractive summarisation task by generating its title. We also present an evaluation using publicly available models as baselines. Our results show that current models are lacking the semantic knowledge required to solve such tasks, shedding light on the need to use better modelling approaches for Maltese.
Chaudhary, Amit Kumar Micallef, Kurt Borg, Claudia
Topic Classification and Headline Generation for Maltese Using a Public News Corpus