  Identifying keyphrases (KPs) from text documents is a fundamental task in
natural language processing and information retrieval. Vast majority of the
benchmark datasets for this task are from the scientific domain containing only
the document title and abstract information. This limits keyphrase extraction
(KPE) and keyphrase generation (KPG) algorithms to identify keyphrases from
human-written summaries that are often very short (approx 8 sentences). This
presents three challenges for real-world applications: human-written summaries
are unavailable for most documents, the documents are almost always long, and a
high percentage of KPs are directly found beyond the limited context of title
and abstract. Therefore, we release two extensive corpora mapping KPs of ~1.3M
and ~100K scientific articles with their fully extracted text and additional
metadata including publication venue, year, author, field of study, and
citations for facilitating research on this real-world problem.

Computation and Language