summarization

Problem
Currently FARMReader will ask users to raise max_seq_length every time some samples are longer than the value set to it. However, this can be confusing if max_seq_length is already set to the maximum value allowed by the model, because raising it further will cause hard-to-read CUDA errors.

See #2177.

Solution
We should find a way to query the model for the maximum va

The models and algorithms in https://github.com/boudinfl/pke#implemented-models are similar to Textrank but not sped up by SpaCy, so it might be a good idea to include them in PyTextRank

PS: There are also other non TextRank-esque algorithms to consider when making this assessment:

• RAKE https://github.com/aneesha/RAKE and https://github.com/csurfer/rake-nltk and https://github.com/vgrabovets

Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.

It would be great if someone can help re-r

A next step for better generation is to implement a beam search for the generation. An example of it can be seen on the huggingface repo here, and this would need adding such a function to the GenerativeT5 model in onnxt5/models.py