Sequential keyphrase generation requires additional focus compared to classic text generation. Sequential keyphrase generation generates a meaning set of phrases by considering the technical and grammatical aspects of the sentence sequentially. Deep learning models are widely used in sequential keyphrase generation due to their ability to handle sequential data using deep neural networks.

  Keyphrase extraction is an important goal in sequential keyphrase generation that requires diverse domain knowledge to obtain reliable annotation for keyphrase identification and generation. Deep learning models faces difficulty in extracting significant key phrase, as it lacks the expertness in understanding different domain knowledge. Transfer learning is an approach of deep learning that utilizes knowledge transfer from one domain to another domain. Deep transfer learning-based sequential keyphrase generation produces optimal and purposeful sentences.

  • In the era of overload information, keyphrase generation imparts highly summative and extractive keyphrases with improvised information utilization efficiency.

  • Deep learning models effectively conduct sequential keyphrase generation, as it focuses on the semantic representation of source text in a sequential manner.

  • Leveraging the unlabeled abundant amount of documents is challenging for training deep neural networks in keyphrase generation.

  • Transfer learning strategies with pre-trained models have been utilized for keyphrase generation from large unlabeled data.

  • Transfer learning, along with deep learning models, conducts keyphrase generation regarding the sequence of the text and imparts better performance.