today is Jan 30, 2023

utility for using transformers summarization models on text docs

The purpose of this package is to provide a simple interface (python API, CLI, gradio web UI) for using summarization models on text documents of arbitrary length.

⚠️ WARNING: This package is a WIP and is not ready for production use. Some things may not work yet. ⚠️


Install using pip:

# create a virtual environment (optional) pip install textsum

The textsum package is now installed in your virtual environment. You can now use the CLI or python API to summarize text docs see the Usage section for more details.

Full Installation

To install all the dependencies (includes PDF OCR, gradio UI demo, optimum, etc), run:

git clone cd textsum # create a virtual environment (optional) pip install -e .[all]

Additional Details

This package uses the clean-text python package, and like the "base" version of the package does not include the GPL-licensed unidecode dependency. If you want to use the unidecode package, install the package as an extra with pip:

pip install textsum[unidecode]

In practice, text cleaning pre-summarization with/without unidecode should not make a significant difference.


There are three ways to use this package:

  1. python API
  2. CLI
  3. Demo App

Python API

To use the python API, import the Summarizer class and instantiate it. This will load the default model and parameters.

You can then use the summarize_string method to summarize a long string of text.

from textsum.summarize import Summarizer summarizer = Summarizer () # loads default model and parameters # summarize a long string out_str = summarizer . summarize_string ( 'This is a long string of text that will be summarized.' ) print ( f 'summary: { out_str } ' )

you can also directly summarize a file:

out_path = summarizer . summarize_file ( '/path/to/file.txt' ) print ( f 'summary saved to { out_path } ' )


To summarize a directory of text files, run the following command:

textsum-dir /path/to/dir

The following options are available:

usage: textsum-dir [-h] [-o OUTPUT_DIR] [-m MODEL_NAME] [-batch BATCH_LENGTH] [-stride BATCH_STRIDE] [-nb NUM_BEAMS] [-l2 LENGTH_PENALTY] [-r2 REPETITION_PENALTY] [--no_cuda] [-length_ratio MAX_LENGTH_RATIO] [-ml MIN_LENGTH] [-enc_ngram ENCODER_NO_REPEAT_NGRAM_SIZE] [-dec_ngram NO_REPEAT_NGRAM_SIZE] [--no_early_stopping] [--shuffle] [--lowercase] [-v] [-vv] [-lf LOGFILE] input_dir

For more information, run:

textsum-dir --help

Demo App

For convenience, a UI demo[^1] is provided using gradio. To ensure you have the dependencies installed, clone the repo and run the following command:

pip install textsum[app]

To run the demo, run the following command:


This will start a local server that you can access in your browser a shareable link will be printed to the console.

[^1]: The demo is currently minimal, but will be expanded in the future to accept other arguments and options.


  • add CLI for summarization of all text files in a directory
  • python API for summarization of text docs
  • add argparse CLI for UI demo
  • put on pypi
  • optimum inference integration, LLM.int8 inference
  • better documentation in the wiki, details on improving performance (speed, quality, memory usage, etc.)

Other ideas? Open an issue or PR!

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