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https://github.com/miso-belica/sumy

Module for automatic summarization of text documents and HTML pages.
https://github.com/miso-belica/sumy

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

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Module for automatic summarization of text documents and HTML pages.

Lists

README 

        
# Automatic text summarizer



[![image](https://github.com/miso-belica/sumy/actions/workflows/run-tests.yml/badge.svg)](https://github.com/miso-belica/sumy/actions/workflows/run-tests.yml)

[![GitPod Ready-to-Code](https://img.shields.io/badge/Gitpod-Ready--to--Code-blue?logo=gitpod)](https://gitpod.io/#https://github.com/miso-belica/sumy) 



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](docs/summarizators.md). I also maintain a list of [alternative implementations](docs/alternatives.md) of the summarizers in various programming languages.



## Is my natural language supported?

There is a [good chance](docs/index.md#Tokenizer) it is. But if not it is [not too hard to add](docs/how-to-add-new-language.md) it.



## Installation



Make sure you have [Python](http://www.python.org/) 3.6+ and

[pip](https://crate.io/packages/pip/)

([Windows](http://docs.python-guide.org/en/latest/starting/install/win/),

[Linux](http://docs.python-guide.org/en/latest/starting/install/linux/))

installed. Run simply (preferred way):



```sh

$ [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.



```sh

$ 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:



```sh

$ 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.



```sh

$ 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`](https://stackoverflow.com/questions/41334622/python-sumy-no-module-named-sumy-parsers-html)) with the code below to test it.



```python

# -*- 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://software.intel.com/en-us/articles/using-natural-language-processing-for-smart-question-generation) - 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

* AutoTL;DR bot for [Lemmy](https://en.wikipedia.org/wiki/Lemmy_(software)) uses sumy: https://github.com/RikudouSage/LemmyAutoTldrBot