From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization

Botond Barta, Dorina Lakatos, Attila Nagy, Milán Konor Nyist, Judit Ács

Abstract
Training summarization models requires substantial amounts of training data. However for less resourceful languages like Hungarian, openly available models and datasets are notably scarce. To address this gap our paper introduces an open-source Hungarian corpus suitable for training abstractive and extractive summarization models. The dataset is assembled from segments of the Common Crawl corpus undergoing thorough cleaning, preprocessing and deduplication. In addition to abstractive summarization we generate sentence-level labels for extractive summarization using sentence similarity. We train baseline models for both extractive and abstractive summarization using the collected dataset. To demonstrate the effectiveness of the trained models, we perform both quantitative and qualitative evaluation. Our models and dataset will be made publicly available, encouraging replication, further research, and real-world applications across various domains.
Anthology ID:
2024.lrec-main.662
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
7503–7509
Language:
URL:
https://aclanthology.org/2024.lrec-main.662
DOI:
Bibkey:
Cite (ACL):
Botond Barta, Dorina Lakatos, Attila Nagy, Milán Konor Nyist, and Judit Ács. 2024. From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 7503–7509, Torino, Italia. ELRA and ICCL.
Cite (Informal):
From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization (Barta et al., LREC-COLING 2024)
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