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In this paper, we formulate keyphrase extraction from scholarly articles as asequence labeling task solved using a BiLSTM-CRF, where the words in the inputtext are represented using deep contextualized embeddings. We evaluate theproposed architecture using both contextualized and fixed word embedding modelson three different benchmark datasets (Inspec, SemEval 2010, SemEval 2017) andcompare with existing popular unsupervised and supervised techniques. Ourresults quantify the benefits of (a) using contextualized embeddings (e.g.BERT) over fixed word embeddings (e.g. Glove); (b) using a BiLSTM-CRFarchitecture with contextualized word embeddings over fine-tuning thecontextualized word embedding model directly, and (c) using genre-specificcontextualized embeddings (SciBERT). Through error analysis, we also providesome insights into why particular models work better than others. Lastly, wepresent a case study where we analyze different self-attention layers of thetwo best models (BERT and SciBERT) to better understand the predictions made byeach for the task of keyphrase extraction.

Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings