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View Code? Open in Web Editor NEWSUPERT: Unsupervised multi-document summarization evaluation & generation
Home Page: https://arxiv.org/abs/2005.03724
SUPERT: Unsupervised multi-document summarization evaluation & generation
Home Page: https://arxiv.org/abs/2005.03724
Hi. Is it possible to train model on GPU?
Problem:
When I run the sample code provided for evaluation, I run for the following error in the file supert.py:
Checking the SBERT documentation, I see that model.encode() only returns embeddings, not the tokens itself.
I tried to use the AutoTokenizer from huggingface in order to return tokens, however I ran into assertion error as length of tokens were not equal to length of embeddings.
Before doing more reverse engineering, I wanted to ask you what you wanted to achieve here and if it is a deprecated usage how can we solve it?
Thank you for your help!
Dear Yang,
It seems the "summariser/utils" folder is missing in the compare_metrics
branch. I am trying to use the "utils" folder in the master
branch as a succedaneum. But there may have some differences. Could you upload it? Thank you.
Hello.
I'd like to know if it's possible to adjust length of output summary?
This error occurs when running evaluate_summary.py
without the optionnal part for one file with 3 summaries (data/input_docs/file_1.txt and data/summaries/summary_1.txt, data/summaries/summary_2.txt, data/summaries/summary_3.txt). My text is smaller than 15 sentences.
Thank you for sharing this nice work!
Could you provide some instructions to reproduce the results of Table 2, 4, and 5 in the paper? Thanks again.
Hi, I'm relatively new to using GitHub and Python... How can I actually run the summarizer on my documents?
I am receiving an error that python can't find "ref_free_metrics" or "generate_summary_rl" when I try to run the code in the readme.
Thanks!
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