miso-belica/sumy

Module for automatic summarization of text documents and HTML pages.

Python

3,700

529 commits

updated Oct 11, 2026

See the code

README

Automatic text summarizer

image GitPod Ready-to-Code

Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains simple evaluation framework for text summaries. Implemented summarization methods are described in the documentation. I also maintain a list of alternative implementations of the summarizers in various programming languages.

Is my natural language supported?

There is a good chance it is. But if not it is not too hard to add it.

Installation

Make sure you have Python 3.8+ installed.

uv is a modern, fast Python package installer and resolver.

$ uv pip install sumy
$ uv pip install git+https://github.com/miso-belica/sumy.git  # for the fresh version

Using pip

$ pip install sumy
$ pip install git+git://github.com/miso-belica/sumy.git  # for the fresh version

For development

If you want to contribute or modify the code:

$ git clone https://github.com/miso-belica/sumy.git
$ cd sumy
$ uv sync --all-extras  # Install dependencies (dev group included automatically)
$ uv run pytest         # Run tests
$ uv run sumy --help    # Run sumy using uv

Usage

Thanks to some good soul out there, the easiest way to try sumy is in your browser at https://huggingface.co/spaces/issam9/sumy_space

Sumy contains command line utility for quick summarization of documents.

$ sumy lex-rank --length=10 --url=https://en.wikipedia.org/wiki/Automatic_summarization # what's summarization?
$ sumy lex-rank --language=uk --length=30 --url=https://uk.wikipedia.org/wiki/Україна
$ sumy luhn --language=czech --url=https://www.zdrojak.cz/clanky/automaticke-zabezpeceni/
$ sumy edmundson --language=czech --length=3% --url=https://cs.wikipedia.org/wiki/Bitva_u_Lipan
$ sumy --help # for more info

Various evaluation methods for some summarization method can be executed by commands below:

$ sumy_eval lex-rank reference_summary.txt --url=https://en.wikipedia.org/wiki/Automatic_summarization
$ sumy_eval lsa reference_summary.txt --language=czech --url=https://www.zdrojak.cz/clanky/automaticke-zabezpeceni/
$ sumy_eval edmundson reference_summary.txt --language=czech --url=https://cs.wikipedia.org/wiki/Bitva_u_Lipan
$ sumy_eval --help # for more info

If you don't want to bother by the installation, you can try it as a container.

$ docker run --rm misobelica/sumy lex-rank --length=10 --url=https://en.wikipedia.org/wiki/Automatic_summarization

Python API

Or you can use sumy like a library in your project. Create file sumy_example.py (don't name it sumy.py) with the code below to test it.

# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division, print_function, unicode_literals

from sumy.parsers.html import HtmlParser
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words


LANGUAGE = "english"
SENTENCES_COUNT = 10


if __name__ == "__main__":
    url = "https://en.wikipedia.org/wiki/Automatic_summarization"
    parser = HtmlParser.from_url(url, Tokenizer(LANGUAGE))
    # or for plain text files
    # parser = PlaintextParser.from_file("document.txt", Tokenizer(LANGUAGE))
    # parser = PlaintextParser.from_string("Check this out.", Tokenizer(LANGUAGE))
    stemmer = Stemmer(LANGUAGE)

    summarizer = Summarizer(stemmer)
    summarizer.stop_words = get_stop_words(LANGUAGE)

    for sentence in summarizer(parser.document, SENTENCES_COUNT):
        print(sentence)

Interesting projects using sumy

I found some interesting projects while browsing the internet or sometimes people wrote me an e-mail with questions, and I was curious how they use the sumy :)

html-extraction
html-extractor
html-page
lsa
nlp
pagerank-algorithm
python
reduction
summarization
summarizer
summary
sumy
text-extraction
textteaser

miso-belica/sumy

Module for automatic summarization of text documents and HTML pages.

Python

3,700

529 commits

updated Oct 11, 2026

See the code

README

Automatic text summarizer

image GitPod Ready-to-Code

Simple library and command line utility for extracting summary from HTML pages or plain texts. The package also contains simple evaluation framework for text summaries. Implemented summarization methods are described in the documentation. I also maintain a list of alternative implementations of the summarizers in various programming languages.

Is my natural language supported?

There is a good chance it is. But if not it is not too hard to add it.

Installation

Make sure you have Python 3.8+ installed.

uv is a modern, fast Python package installer and resolver.

$ uv pip install sumy
$ uv pip install git+https://github.com/miso-belica/sumy.git  # for the fresh version

Using pip

$ pip install sumy
$ pip install git+git://github.com/miso-belica/sumy.git  # for the fresh version

For development

If you want to contribute or modify the code:

$ git clone https://github.com/miso-belica/sumy.git
$ cd sumy
$ uv sync --all-extras  # Install dependencies (dev group included automatically)
$ uv run pytest         # Run tests
$ uv run sumy --help    # Run sumy using uv

Usage

Thanks to some good soul out there, the easiest way to try sumy is in your browser at https://huggingface.co/spaces/issam9/sumy_space

Sumy contains command line utility for quick summarization of documents.

$ sumy lex-rank --length=10 --url=https://en.wikipedia.org/wiki/Automatic_summarization # what's summarization?
$ sumy lex-rank --language=uk --length=30 --url=https://uk.wikipedia.org/wiki/Україна
$ sumy luhn --language=czech --url=https://www.zdrojak.cz/clanky/automaticke-zabezpeceni/
$ sumy edmundson --language=czech --length=3% --url=https://cs.wikipedia.org/wiki/Bitva_u_Lipan
$ sumy --help # for more info

Various evaluation methods for some summarization method can be executed by commands below:

$ sumy_eval lex-rank reference_summary.txt --url=https://en.wikipedia.org/wiki/Automatic_summarization
$ sumy_eval lsa reference_summary.txt --language=czech --url=https://www.zdrojak.cz/clanky/automaticke-zabezpeceni/
$ sumy_eval edmundson reference_summary.txt --language=czech --url=https://cs.wikipedia.org/wiki/Bitva_u_Lipan
$ sumy_eval --help # for more info

If you don't want to bother by the installation, you can try it as a container.

$ docker run --rm misobelica/sumy lex-rank --length=10 --url=https://en.wikipedia.org/wiki/Automatic_summarization

Python API

Or you can use sumy like a library in your project. Create file sumy_example.py (don't name it sumy.py) with the code below to test it.

# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division, print_function, unicode_literals

from sumy.parsers.html import HtmlParser
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer as Summarizer
from sumy.nlp.stemmers import Stemmer
from sumy.utils import get_stop_words


LANGUAGE = "english"
SENTENCES_COUNT = 10


if __name__ == "__main__":
    url = "https://en.wikipedia.org/wiki/Automatic_summarization"
    parser = HtmlParser.from_url(url, Tokenizer(LANGUAGE))
    # or for plain text files
    # parser = PlaintextParser.from_file("document.txt", Tokenizer(LANGUAGE))
    # parser = PlaintextParser.from_string("Check this out.", Tokenizer(LANGUAGE))
    stemmer = Stemmer(LANGUAGE)

    summarizer = Summarizer(stemmer)
    summarizer.stop_words = get_stop_words(LANGUAGE)

    for sentence in summarizer(parser.document, SENTENCES_COUNT):
        print(sentence)

Interesting projects using sumy

I found some interesting projects while browsing the internet or sometimes people wrote me an e-mail with questions, and I was curious how they use the sumy :)

html-extraction
html-extractor
html-page
lsa
nlp
pagerank-algorithm
python
reduction
summarization
summarizer
summary
sumy
text-extraction
textteaser

Project health

Overall

5.4/10

Maintained

10/10

Code review

0/10
OpenSSF Scorecard

PyPI

Latest release

0.13.0 · Aug 12, 2026

Releases this year

2

Significant stargazers

Wilson Yan

222 followers · starred Dec 2022

Mikhail Korobov

1,185 followers · starred Feb 2018

Gram

365 followers · starred Sep 2019

Kamal Raj Kanakarajan

490 followers · starred Feb 2018