SumeCzech: Large Czech News-Based Summarization Dataset

Kód výsledku v 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>

Výsledek na webu

<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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Jazyk výsledku

angličtina

Název v původním jazyce

SumeCzech: Large Czech News-Based Summarization Dataset

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

SumeCzech: Large Czech News-Based Summarization Dataset

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2018

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

8

Strana od-do

3488-3495

Název nakladatele

European Language Resources Association

Místo vydání

Paris, France

Místo konání akce

Miyazaki, Japan

Datum konání akce

7. 5. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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