OAGS Title Generation Dataset

Title:

OAGS Title Generation Dataset

Creator:

Çano, Erion

Contributor:

Ministerstvo školství, mládeže a tělovýchovy České republiky@@CZ.02.2.69/0.0/0.0/16_027/0008495@@OP VVV Mezinárodní mobilita výzkumných pracovníků Univerzity Karlovy@@nationalFunds@@ and European Union@@H2020-ICT-2018-2-825460@@ELITR - European Live Translator@@euFunds@@info:eu-repo/grantAgreement/EC/H2020/825460

Publisher:

Charles University, Faculty of Mathematics and Physics, Institute of Formal and Applied Linguistics (UFAL)

Identifier:

http://hdl.handle.net/11234/1-3043

Subject:

Title Generation Dataset, Abstractive Text Summarization, and Scientific Papers Corpus

Type:

text and corpus

Description:

OAGS is a title generation dataset consisting of 34993700 abstracts and titles from scientific articles. Texts were lowercased and tokenized with Stanford CoreNLP tokenizer. No other preprocessing steps were applied in this release version. Dataset records (samples) are stored as JSON lines in each text file. The data is derived from OAG data collection (https://aminer.org/open-academic-graph) which was released under ODC-BY licence. This data (OAGS Title Generation Dataset) is released under CC-BY licence (https://creativecommons.org/licenses/by/4.0/). If using it, please cite the following paper: Çano, Erion and Bojar, Ondřej, 2019, "Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study", INLG 2019, The 12th International Conference on Natural Language Generation, November 2019, Tokyo, Japan. To reproduce the experiments in the above paper, you can use oags_train1.txt, oags_train2.txt, oags_train3.txt, oags_test.txt and oags_val.txt files. If you need more data samples you can get them from oags_train_backup.txt and oags_val-test_backup.txt.

Language:

English

Rights:

Creative Commons - Attribution 4.0 International (CC BY 4.0)
http://creativecommons.org/licenses/by/4.0/
PUB

Relation:

info:eu-repo/grantAgreement/EC/H2020/825460
https://www.aclweb.org/anthology/W19-8630/
http://hdl.handle.net/11234/1-3079

Harvested from:

LINDAT/CLARIAH-CZ repository

Metadata only:

false

Date:

2019-09