Incorporating Multi-granularity Extractive Features for Keyphrase Generation

Abstract

Keyphrase is a set of phrases that summarizes the core theme and key content of a given text.At present,information overload is becoming more and more serious,it is crucial to predict phrases with their central ideas for a given large amount of textual information.Therefore,keyphrase prediction,as one of the basic tasks of natural language processing,has received more and more attention from research scholars.Its corresponding methods mainly contain two categories,namely keyphrase extraction and keyphrase generation.Keyphrase extraction is the fast and accurate extraction of salient phrases that appear in the given text.Unlike keyphrase extraction,keyphrase generation predicts both phrases that appear in the given text and those do not appear in the given text.In summary,both have their advantages and disadvantages.However,most of the existing work on keyphrase ge-neration has ignored the potential benefits that extractive features may bring to keyphrase generation models.Extractive features can indicate important fragments of the original text and play an important role for the model to learn the deep semantic representation of the original text.Therefore,combining the advantages of extractive and generative approaches,this paper proposes a new keyphrase generation model incorporating multi-granularity extractive features(MGE-Net).Compared with recent keyphrase ge-neration models on a series of publicly available datasets,the proposed model achieves significant performance improvements in most evaluation metrics.

Keywords

• natural language processing|sequence-to-sequence|keyphrase generation|extractive features|multi-task learning