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Keyphrase generation aims at generating important phrases (keyphrases) thatbest describe a given document. In scholarly domains, current approaches havelargely used only the title and abstract of the articles to generatekeyphrases. In this paper, we comprehensively explore whether the integrationof additional information from the full text of a given article or fromsemantically similar articles can be helpful for a neural keyphrase generationmodel or not. We discover that adding sentences from the full text,particularly in the form of the extractive summary of the article cansignificantly improve the generation of both types of keyphrases that areeither present or absent from the text. Experimental results with three widelyused models for keyphrase generation along with one of the latest transformermodels suitable for longer documents, Longformer Encoder-Decoder (LED) validatethe observation. We also present a new large-scale scholarly dataset FullTextKPfor keyphrase generation. Unlike prior large-scale datasets, FullTextKPincludes the full text of the articles along with the title and abstract. Werelease the source code at https://github.com/kgarg8/FullTextKP.

Keyphrase Generation Beyond the Boundaries of Title and Abstract