Comments (4)
Thank you for the quick response Housen and for the direction.
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Whoops! Yes, the number of top sentences to select was hardcoded to 2 in the ExtractiveSummarizer.predict()
function. Yes, the top n
sentences are returned. I've updated the library so the ExtractiveSummarizer.predict()
function has a num_summary_sentences
argument to specify the number of sentences in the output summary. The default is 3 sentences. Let me know if this works 😄.
from transformersum.
Hi, Is there any upper limit for num_summary_sentences
? Wanted to create a summary of 100 sentences from an article of 200+ sentences using MobileBERT. It gives only 8 sentences (or below if num_summary_sentences
is smaller), regardless of num_summary_sentences
value. Please advise. Thank you.
from transformersum.
Yes, there is an upper limit since the decoder of most BART-Like models can only output 512 tokens. Transformers generally cannot handle long sequences of input or output. The Longformer would be your best option since it can handle an input of about 8000/16000 (depending on the version) tokens but still only outputs 512 tokens. If you wanted a summary that long then you should use a standard algorithm like TextRank.
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Related Issues (20)
- TypeError: __init__() got an unexpected keyword argument 'gradient_checkpointing' HOT 1
- predictions_website.py raises AttributeError: '_LazyAutoMapping' object has no attribute '_mapping' HOT 6
- ModuleNotFoundError: No module named 'extractive' HOT 1
- AttributeError: '_LazyAutoMapping' object has no attribute '_mapping' HOT 1
- Abstractive BART Model , RuntimeError: The size of tensor a (64000) must match the size of tensor b (64001) at non-singleton dimension 1
- ValueError: Connection error, and we cannot find the requested files in the cached path. Please try again or make sure your Internet connection is on. HOT 3
- error when training an extractive summarization model HOT 2
- Found keys that are in the model state dict but not in the checkpoint HOT 3
- Suggest about the index order of extractive results
- A Chinese solution for TransformerSum-extractive, and I've implemented your work in my project HOT 1
- After extractive training, a process on one GPU won't terminate automatically.
- Fine-tuning/Inference commands for "roberta-base-ext-sum"
- '--data_type' is not accepted when running main.py (extractive mode)
- Why tokenize twice?
- TypeError: forward() got an unexpected keyword argument 'source'
- Instruction for fine tune
- Installation via Pip
- Some versioning problems when installing the environment HOT 2
- pytorch_lightning.callbacks update HOT 1
- RoBERTa & Longformer extractive model checkpoints availability
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