Model: aglazkova/bart_multitask_finetuned_for_title_and_keyphrase_generation
Author: aglazkova
Pipeline: text-generation
Library: transformers
Tags: transformers, pytorch, bart, text2text-generation, en, dataset:midas/krapivin, dataset:midas/inspec, arxiv:1910.13461, endpoints_compatible, region:us
Card metadata: datasets: midas/krapivin, midas/inspec
language: en
widget: 
 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 biomedical domains and news texts. We explore the role of transfer learning between different domains to improve the model performance on small text corpora., 
 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 biomedical domains and news texts. We explore the role of transfer learning between different domains to improve the model performance on small text corpora.
library_name: transformers
pipeline_tag: text2text-generation
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Created: 2023-05-16 10:54:34