# summanlp/textrank

TextRank implementation for Python 3.

Repository: https://github.com/summanlp/textrank
Canonical: https://ross.abutalabs.com/products/textrank
Homepage: https://pypi.org/project/summa/
Language: Python
License: MIT
License Family: permissive
Topics: textrank, summarization, keywords, python, nlp, natural-language-processing, text-summarization
Last push: 2023-03-28T13:38:58+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 4362, "days_push": 1254, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1269, forks 247 (observed 2026-08-28T04:04:11.704122+00:00)

## What it is
A Python 3 implementation of the TextRank algorithm for extractive text summarization and keyword extraction. It is the library published on PyPI as 'summa'.

## Use cases
- summarize long articles automatically
- extract keywords from a document
- rank sentences by importance in text
- build extractive summarization into a python app
- get top keyphrases from news articles

## When to choose
- you need a lightweight, dependency-free TextRank implementation in Python
- you want extractive (not abstractive) summarization
- you need fast keyword extraction without deep learning models

## When to avoid
- you need abstractive or neural summarization quality
- you need multilingual support beyond what TextRank handles well
- you want an actively developed library with recent features

## Facets
- artifact type: library
- maturity: maintenance
- function: nlp, parser
- domain: machine-learning
- platform: python
- tags: textrank, text-summarization, keyword-extraction, summa, natural-language-processing

## Member repositories
- summanlp/textrank (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:11.704122+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T05:03:34.157573+00:00, confidence not recorded.
  - readme: https://github.com/summanlp/textrank (fetched 2026-08-28T04:04:11.704122+00:00, sha 2b7814d3fca2)
  - homepage: https://pypi.org/project/summa/ (fetched 2026-08-29T12:15:39.911808+00:00, sha 4b4e8fead74a)
- Data as of 2026-08-30T08:39:29.467469+00:00.