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Topic Classification and Headline Generation for Maltese Using a Public News Corpus

Paper · 2024 · Amit Kumar Chaudhary, Kurt Micallef and Claudia Borg

Summary

Maltese is a low-resource language whose public evaluation data is mostly syntactic: POS tagging, UD parsing and NER. The one semantic dataset, for sentiment, has only 851 sentences. The authors build two larger semantic benchmarks without paying for large-scale annotation. Instead they reuse metadata that news websites already attach to articles. Their source is the press_mt part of Korpus Malti v4.0, which they clean heavily. From it they derive:

  • a multi-label topic classification task, using portal tags that native speakers merged into a smaller standard set;
  • a headline generation task, framed as abstractive summarisation from article body to title.

They train baseline transformer models on both tasks. For classification these include the Maltese encoders BERTu and mBERTu plus English BERT and mBERT. The main finding is that current models lack the semantic competence these tasks demand, so Maltese needs better modelling approaches. Both datasets are released openly, which gives the language a reusable, non-translated semantic benchmark.

Key points

  • Source data is press_mt from Korpus Malti v4.0. Filtering shrinks it from 44,823 to 25,403 articles by removing non-article pages, duplicates, texts under ten words, leftover CSS/JS and repeated portal boilerplate.
  • The topic labels start as 233 raw tags, which are reduced to 17 categories over 15,374 articles. The steps are:
    • manually merging tags;
    • dropping articles that carry only generic tags;
    • dropping tags with fewer than 100 articles;
    • removing tags that overlap with another tag more than 75% of the time (e.g., Family co-occurred 93% with Culture).
  • Splits are 70/15/15. The classification split uses iterative stratification from scikit-multilearn.
  • The headline dataset has 25,403 article–title pairs from 8 portals. It also keeps the base URL so outputs can be conditioned on portal style.
  • The data is released as MLRS/maltese_news_categories and MLRS/maltese_news_headlines on Hugging Face.

Interesting ideas

  • I1. Instead of annotating from scratch, existing editorial metadata (tags, titles) becomes free weak supervision, with humans only standardising the label inventory.
  • I2. Labels that almost always co-occur with another label are pruned using a simple overlap threshold, because they add no discriminative signal.
  • I3. Cleaning uses source-specific regexes and removes repeated boilerplate phrases per portal, rather than one generic filter.
  • I4. The source identifier (base URL) is kept as a conditioning signal, so headline generation can follow a publication's house style.
  • I5. The authors argue that machine-translated benchmarks measure translation artefacts more than language understanding, especially where MT quality is poor.

For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

  • I1 (learn): English news tag/title metadata could serve as cheap silver labels for checking whether frame and world extraction captures what an article is about.
  • I2 (world): the same co-occurrence test could flag redundant verb classes or ontology categories in the compiled knowledge layer before they bloat the inventory.
  • I3 (tokens): per-source normalisation rules before the tokenizer would keep scraped noise like "Read in English" from producing junk tokens and spurious UD trees.
  • I4 (back): text generation from the meaning graph could take an explicit register/style tag, keeping content and surface style separately controllable and explainable.
  • I5 (answer): test sets for the readers should come from natively written English material, not converted text, so that scores reflect actual comprehension.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 2,908 worksSummarization · 232 works

Cite

Cite as Amit Kumar Chaudhary, Kurt Micallef and Claudia Borg. 2024. Topic Classification and Headline Generation for Maltese Using a Public News Corpus. Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024).

The paper itself, not this page. BibTeX from the library's record of the work.

@inproceedings{chaudhary2024topic,
  title = {{Topic Classification and Headline Generation for Maltese Using a Public News Corpus}},
  author = {Amit Kumar Chaudhary, Kurt Micallef and Claudia Borg},
  booktitle = {Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)},
  year = {2024},
  doi = {10.63317/2gp8noswn4ij},
  url = {https://doi.org/10.63317/2gp8noswn4ij}
}

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