Two Huge Title and Keyword Generation Corpora of Research Articles

Erion Çano, Ondřej Bojar


Abstract
Recent developments in sequence-to-sequence learning with neural networks have considerably improved the quality of automatically generated text summaries and document keywords, stipulating the need for even bigger training corpora. Metadata of research articles are usually easy to find online and can be used to perform research on various tasks. In this paper, we introduce two huge datasets for text summarization (OAGSX) and keyword generation (OAGKX) research, containing 34 million and 23 million records, respectively. The data were retrieved from the Open Academic Graph which is a network of research profiles and publications. We carefully processed each record and also tried several extractive and abstractive methods of both tasks to create performance baselines for other researchers. We further illustrate the performance of those methods previewing their outputs. In the near future, we would like to apply topic modeling on the two sets to derive subsets of research articles from more specific disciplines.
Anthology ID:
2020.lrec-1.823
Volume:
Proceedings of the Twelfth Language Resources and Evaluation Conference
Month:
May
Year:
2020
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
6663–6671
Language:
English
URL:
https://aclanthology.org/2020.lrec-1.823
DOI:
Bibkey:
Cite (ACL):
Erion Çano and Ondřej Bojar. 2020. Two Huge Title and Keyword Generation Corpora of Research Articles. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6663–6671, Marseille, France. European Language Resources Association.
Cite (Informal):
Two Huge Title and Keyword Generation Corpora of Research Articles (Çano & Bojar, LREC 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.lrec-1.823.pdf