fast_abs_rl
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Code for ACL 2018 paper: "Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting. Chen and Bansal"
deer how to solve it?in the rl.py autograd.backward( [critic_loss] + losses, [torch.ones(1).to(critic_loss.device)]*(1+len(losses)), ) raise that problem
Hello Chen, Thanks for providing this code. It seems really helpful for my current research. However, I am having issues with making this code work. I have setup the environment...
scatter_add
In scatter_add function "source" parameter is not working , "src" should be used instead of "source".
Hi @ChenRocks , can you please check this issue? I tried to train your model with my own dataset in Vietnamese. It's OK at step 1 (train_word2vec.py) and step 2...
Hi developer, Based on your model, the abstractor should be able to generate summaries, then together with ground truth summaries, the reward is calculated and fed to extractor. So how...
I am trying to run it using windows anaconda environment. i have install perl.it is running. Getting these error. Can anyone please help me.
I have been banging my head against a wall to get this working with my own dataset. I have successfully trained the word2vec model and the extraction labels but run...
In https://github.com/ChenRocks/fast_abs_rl/blob/master/make_extraction_labels.py#L47 you split articles and abstracts into sentences to compute the labels for extraction. However `t.split()` splits into words, and not sentences. Is this a bug? If not, how...