To help you get started, we've collected the most common ways that sumy is being used within popular public projects.

miso-belica / sumy / tests / test_models / test_tf.py View on Github

def test_normalized_words_frequencies():
    words = "a b c d e c b d c e e d e d e".split()
    model = TfDocumentModel(tuple(words))

    assert model.normalized_term_frequency("a") == pytest.approx(1/5)
    assert model.normalized_term_frequency("b") == pytest.approx(2/5)
    assert model.normalized_term_frequency("c") == pytest.approx(3/5)
    assert model.normalized_term_frequency("d") == pytest.approx(4/5)
    assert model.normalized_term_frequency("e") == pytest.approx(5/5)
    assert model.normalized_term_frequency("z") == pytest.approx(0.0)

    assert model.most_frequent_terms() == ("e", "d", "c", "b", "a")

View more ways to use sumy

Automatic text summarizer

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 3.6+ and pip (Windows, Linux) installed. Run simply (preferred way):

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

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 😃

• Learning to generate questions from text - https://github.com/adityasarvaiya/Automatic_Question_Generation
• Summarize your video to any duration - https://github.com/aswanthkoleri/VideoMash and similar https://github.com/OpenGenus/vidsum
• Tool for collectively summarizing large discussions - https://github.com/amyxzhang/wikum