Papers with neural keyphrase generation models

2 papers
Redefining Absent Keyphrases and their Effect on Retrieval Effectiveness (2021.naacl-main)

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Challenge: Neural keyphrase generation models can output absent keyphrases, which are keyphrase that do not appear in the source text.
Approach: They propose a finer-grained categorization scheme that sheds more light on the impact of absent keyphrases on scientific document retrieval.
Outcome: The proposed model shows that only 20% of the words that make up keyphrases actually serve as document expansion, but this small fraction behind much of the gains observed in retrieval effectiveness.
One Size Does Not Fit All: Generating and Evaluating Variable Number of Keyphrases (2020.acl-main)

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Challenge: Existing models for keyphrase generation do not provide a desideratum for the number of keyphrases in texts.
Approach: They propose a recurrent generative model that generates multiple keyphrases as delimiter-separated sequences.
Outcome: The proposed model outperforms baseline models on all datasets.

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