Neural Keyphrase Generation: Analysis and Evaluation
This work addresses the lack of analysis in neural keyphrase generation, providing insights for researchers and practitioners in natural language processing.
The paper analyzed the performance and behavior of three neural keyphrase generation models, revealing tendencies in prediction confidence and model calibration, and introduced SoftKeyScore, a novel metric that outperforms standard F1 for evaluating keyphrase similarity.
Keyphrase generation aims at generating topical phrases from a given text either by copying from the original text (present keyphrases) or by producing new keyphrases (absent keyphrases) that capture the semantic meaning of the text. Encoder-decoder models are most widely used for this task because of their capabilities for absent keyphrase generation. However, there has been little to no analysis on the performance and behavior of such models for keyphrase generation. In this paper, we study various tendencies exhibited by three strong models: T5 (based on a pre-trained transformer), CatSeq-Transformer (a non-pretrained Transformer), and ExHiRD (based on a recurrent neural network). We analyze prediction confidence scores, model calibration, and the effect of token position on keyphrases generation. Moreover, we motivate and propose a novel metric framework, SoftKeyScore, to evaluate the similarity between two sets of keyphrases by using softscores to account for partial matching and semantic similarity. We find that SoftKeyScore is more suitable than the standard F1 metric for evaluating two sets of given keyphrases.