Papers by Erwin Daniel Lopez Zapata

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Challenge:	Unsupervised keyphrase extraction methods require large amounts of labeled data and are often domainspecific, limiting their practical applicability.
Approach:	They propose an unsupervised keyphrase extraction method that leverages self-attention maps from a Large Language Model to estimate the importance of candidate phrases.
Outcome:	The proposed method outperforms baseline models on four datasets and is highly efficient on three of four dataset.