I wanted to use pytextrank together with spacy_udpipe to get keywords from texts in other languages (see https://stackoverflow.com/questions/59824405/spacy-udpipe-with-pytextrank-to-extract-keywords-from-non-english-text) but I realized, that udpipe-spacy somehow "overrides" the original spacy's pipeline so the noun_chunks are not generated (btw: the noun_chunks are created in lang/en/syntax_itera

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

Currently we can build a histogram by specifying the edges.
We can also specify a number of bins, and the edges are chosen automatically using the extrema.
It would be helpful to also have an option for specifying bin dimensions to be used uniformly, where the edges would again be chosen using the extrema.

How to restrict summary length in pyrouge ?

The original ROUGE supports this feature [-b n-bytes|-l n-words]

Usage: ROUGE-1.5.4.pl
[-a (evaluate all systems)]
[-c cf]
[-d (print per evaluation scores)]
[-e ROUGE_EVAL_HOME]
[-h (usage)]
[-b n-bytes|-l n-words]
[-m (use Porter stemmer)]
[-n max-ngram]

Refactor the examples which show WordNet use (e.g., lemmas, synsets) to remove the TextBlob and NLTK dependencies, replacing them with:

• https://twitter.com/spacy_io/status/1062487385773170691