Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings
Table 5: Comparison with baseline methods (F1-score)
['[EMPTY]', 'Inspec', 'SE-2010', 'SE-2017']
Lastly, we compare our best performing model (BiLSTM-CRF with SciBERT embeddings) against four baseline methods: SGRank, SingleRank, TextRank, and KEA. As expected, our model significantly outperforms all the baseline methods for all three datasets. Of the four baseline methods evaluated here, SGRank achieved the best results. This observation holds true irrespective of the embeddings used. For example, on Inpsec data, the worst performing BiLSTM-CRF model (Glove at 0.457) is still significantly better than the best performing baseline model (SGRank at 0.271).