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bart_multitask_finetuned_for_title_and_keyphrase_generation

Text2Text Generation

Inference Endpoints

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kdvisdjf rkeogkw commited on May 16, 2023

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1 Parent(s): 03ff273

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@@ -6,7 +6,7 @@ language:
 - en
 widget:
 - text: >-
-    <|KEYPHRASES|> In this paper, we investigate cross-domain limitations of
     keyphrase generation using the models for abstractive text summarization. We
     present an evaluation of BART fine-tuned for keyphrase generation across
     three types of texts, namely scientific texts from computer science and
@@ -14,7 +14,7 @@ widget:
     between different domains to improve the model performance on small text
     corpora.
 - text: >-
-    <|TITLE|> In this paper, we investigate cross-domain limitations of
     keyphrase generation using the models for abstractive text summarization. We
     present an evaluation of BART fine-tuned for keyphrase generation across
     three types of texts, namely scientific texts from computer science and

 - en
 widget:
 - text: >-
+    <|TITLE|> In this paper, we investigate cross-domain limitations of
     keyphrase generation using the models for abstractive text summarization. We
     present an evaluation of BART fine-tuned for keyphrase generation across
     three types of texts, namely scientific texts from computer science and
     between different domains to improve the model performance on small text
     corpora.
 - text: >-
+    <|KEYPHRASES|> In this paper, we investigate cross-domain limitations of
     keyphrase generation using the models for abstractive text summarization. We
     present an evaluation of BART fine-tuned for keyphrase generation across
     three types of texts, namely scientific texts from computer science and