Keyphrase Generation: A Text Summarization Struggle

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10405617" target="_blank" >RIV/00216208:11320/19:10405617 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.aclweb.org/anthology/N19-1070" target="_blank" >https://www.aclweb.org/anthology/N19-1070</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.18653/v1/N19-1070" target="_blank" >10.18653/v1/N19-1070</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Keyphrase Generation: A Text Summarization Struggle

Popis výsledku v původním jazyce

Authors&apos; keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of training data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the much simpler unsupervised methods or the existing supervised ones.

Název v anglickém jazyce

Keyphrase Generation: A Text Summarization Struggle

Popis výsledku anglicky

Authors&apos; keyphrases assigned to scientific articles are essential for recognizing content and topic aspects. Most of the proposed supervised and unsupervised methods for keyphrase generation are unable to produce terms that are valuable but do not appear in the text. In this paper, we explore the possibility of considering the keyphrase string as an abstractive summary of the title and the abstract. First, we collect, process and release a large dataset of scientific paper metadata that contains 2.2 million records. Then we experiment with popular text summarization neural architectures. Despite using advanced deep learning models, large quantities of training data and many days of computation, our systematic evaluation on four test datasets reveals that the explored text summarization methods could not produce better keyphrases than the much simpler unsupervised methods or the existing supervised ones.

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

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název statě ve sborníku

The 17th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

ISBN

978-1-950737-13-0

ISSN

—

e-ISSN

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Počet stran výsledku

7

Strana od-do

666-672

Název nakladatele

NAACL-HLT 2019

Místo vydání

Minneapolis, USA

Místo konání akce

Minneapolis, USA

Datum konání akce

2. 6. 2019

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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