Barta, Botond and Lakatos, Dorina Petra and Nagy, A and Nyist, M K and Ács, Judit (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). Proceedings - International Conference on Computational Linguistics, COLING; LREC proceedings . ELRA, Online kiadás, pp. 7503-7509. ISBN 9782493814104

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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 HunSum-2 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 dataset, models and code are publicly available, encouraging replication, further research, and real-world applications across various domains. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

Item Type:	Book Section
Uncontrolled Keywords:	Hungarians; Hungarian; Training data; Open-source; Deduplication; Sentence similarity; abstractive summarization; abstractive summarization; extractive summarization; Sentence level; Summarization models; Extractive and abstractive summarizations; Extractive summarizations;
Subjects:	Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány
Divisions:	Informatics Laboratory
SWORD Depositor:	MTMT Injector
Depositing User:	MTMT Injector
Date Deposited:	01 Aug 2024 08:57
Last Modified:	01 Aug 2024 08:57
URI:	https://eprints.sztaki.hu/id/eprint/10771

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