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Recent developments in sequence-to-sequence learning with neural networkshave considerably improved the quality of automatically generated textsummaries and document keywords, stipulating the need for even bigger trainingcorpora. Metadata of research articles are usually easy to find online and canbe used to perform research on various tasks. In this paper, we introduce twohuge datasets for text summarization (OAGSX) and keyword generation (OAGKX)research, containing 34 million and 23 million records, respectively. The datawere retrieved from the Open Academic Graph which is a network of researchprofiles and publications. We carefully processed each record and also triedseveral extractive and abstractive methods of both tasks to create performancebaselines for other researchers. We further illustrate the performance of thosemethods previewing their outputs. In the near future, we would like to applytopic modeling on the two sets to derive subsets of research articles from morespecific disciplines.

Two Huge Title and Keyword Generation Corpora of Research Articles