ml6team
/

keyphrase-generation-keybart-inspec

@@ -38,9 +38,11 @@ Keyphrase extraction is a technique in text analysis where you extract the impor
 Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
 Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement.
 ## 📓 Model Description
 This model is a fine-tuned KeyBART model on the Inspec dataset.
 You can find more information about the architecture in this paper: https://arxiv.org/abs/2112.08547.
 Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
@@ -50,7 +52,7 @@ Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learnin
 * This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.
 * Only works for English documents.
 * For a custom model, please consult the training notebook for more information (link incoming).
-* Sometimes the output can make no sense.
 ### ❓ How to use
 ```python

 Currently, classical machine learning methods, that use statistics and linguistics, are widely used for the extraction process. The fact that these methods have been widely used in the community has the advantage that there are many easy-to-use libraries.
 Now with the recent innovations in deep learning methods (such as recurrent neural networks and transformers, GANS, …), keyphrase extraction can be improved. These new methods also focus on the semantics and context of a document, which is quite an improvement.
+Thanks to the introduction of neural networks, it's also possible to generate related keyphrases based on a given text document. This is useful, for example, when an author wants to make his work findable.
 ## 📓 Model Description
 This model is a fine-tuned KeyBART model on the Inspec dataset.
+KeyBART focuses on learning a better representation of keyphrases in a generative setting. It produces the keyphrases associated with the input. This is accomplished by predicting the original input based on a changed input. The input is changed by token masking, keyphrase masking and keyphrase replacement. [10] This model can already be used without any fine-tuning, but can be fine-tuned if needed.
 You can find more information about the architecture in this paper: https://arxiv.org/abs/2112.08547.
 Kulkarni, Mayank, Debanjan Mahata, Ravneet Arora, and Rajarshi Bhowmik. "Learning Rich Representation of Keyphrases from Text." arXiv preprint arXiv:2112.08547 (2021).
 * This keyphrase generation model is very domain-specific and will perform very well on abstracts of scientific papers. It's not recommended to use this model for other domains, but you are free to test it out.
 * Only works for English documents.
 * For a custom model, please consult the training notebook for more information (link incoming).
+* Sometimes the output doesn't make any sense.
 ### ❓ How to use
 ```python