SumeCzech: Large Czech News-Based Summarization Dataset
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F18%3A10390209" target="_blank" >RIV/00216208:11320/18:10390209 - isvavai.cz</a>
Result on the web
<a href="http://www.lrec-conf.org/proceedings/lrec2018/summaries/825.html" target="_blank" >http://www.lrec-conf.org/proceedings/lrec2018/summaries/825.html</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
SumeCzech: Large Czech News-Based Summarization Dataset
Original language description
Document summarization is a well-studied NLP task. With the emergence of artificial neural network models, the summarization performance is increasing, as are the requirements on training data. However, only a few datasets are available for Czech, none of them particularly large. Additionally, summarization has been evaluated predominantly on English, with the commonly used ROUGE metric being English-specific. In this paper, we try to address both issues. We present SumeCzech, a Czech news-based summarization dataset. It contains more than a million documents, each consisting of a headline, a several sentences long abstract and a full text. The dataset can be downloaded using the provided scripts available at http://hdl.handle.net/11234/1-2615. We evaluate several summarization baselines on the dataset, including a strong abstractive approach based on Transformer neural network architecture. The evaluation is performed using a language-agnostic variant of ROUGE.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2018
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 11th International Conference on Language Resources and Evaluation (LREC 2018)
ISBN
979-10-95546-00-9
ISSN
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e-ISSN
neuvedeno
Number of pages
8
Pages from-to
3488-3495
Publisher name
European Language Resources Association
Place of publication
Paris, France
Event location
Miyazaki, Japan
Event date
May 7, 2018
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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