schnitsum 0.4.0

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Schnitsum: Easy to use neural network based summarization models

This package enables to generate summaries of you documents of interests.

Currently, we support following models,

• BART (large) fine-tuned on computer science papers (ref. SciTLDR).
  • Model name: sobamchan/bart-large-scitldr
• BART (large) fine-tuned on computer science papers (ref. SciTLDR). Then distilled (by shrink and fine-tune) to have 65% parameters less.
  • Model name: sobamchan/bart-large-scitldr-distilled-3-3
• BART (large) fine-tuned on computer science papers (ref. SciTLDR). Then distilled (by shrink and fine-tune) to have 37% parameters less.
  • Model name: sobamchan/bart-large-scitldr-distilled-12-3

we are planning to expand coverage soon to other sizes, domains, languages, models soon.

Installation

pip install schnitsum  # or poetry add schnitsum

This will let you generate summaries with CPUs only, if you want to utilize your GPUs, please follow the instruction by PyTorch, here.

Usage

From Command Line

% Pass document as an argument and print the summary
> schnitsum --model-name sobamchan/bart-large-scitldr-distilled-3-3 --text "Text to summarize"

% Pass documents as a file and save summaries in a file.
% Input file needs to contain documents line by line. [example](https://github.com/sobamchan/schnitsum/blob/main/examples/docs.txt)
> schnitsum --model-name sobamchan/bart-large-scitldr-distilled-3-3 --file docs.txt --opath sums.txt

From Python

from schnitsum import SchnitSum
model = SchnitSum("sobamchan/bart-large-scitldr-distilled-3-3")

docs = [
    "Document you want to summarize."
]

summaries = model(docs)
print(summaries)