@inproceedings{barta-etal-2024-news-summaries,
title = "From News to Summaries: Building a {H}ungarian Corpus for Extractive and Abstractive Summarization",
author = "Barta, Botond and
Lakatos, Dorina and
Nagy, Attila and
Nyist, Mil{\'a}n Konor and
{\'A}cs, Judit",
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.662",
pages = "7503--7509",
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.",
}
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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 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.</abstract>
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%0 Conference Proceedings
%T From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization
%A Barta, Botond
%A Lakatos, Dorina
%A Nagy, Attila
%A Nyist, Milán Konor
%A Ács, Judit
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F barta-etal-2024-news-summaries
%X 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.
%U https://aclanthology.org/2024.lrec-main.662
%P 7503-7509
Markdown (Informal)
[From News to Summaries: Building a Hungarian Corpus for Extractive and Abstractive Summarization](https://aclanthology.org/2024.lrec-main.662) (Barta et al., LREC-COLING 2024)
ACL