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Pytextrank

PyTextRank is a Python library that implements TextRank and related text-graph algorithms as a spaCy pipeline extension for extracting ranked phrases, supporting extractive summarization and helping infer concepts from unstructured text.

Independent OpenViST project profile, based on linked sources.

Pytextrank — OpenViST project profile in AI Developer Tools & SDKs.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use PyTextRank when a Python text-processing workflow needs graph-based phrase ranking or extractive summarization within a spaCy pipeline.

Who it is for

  • Python developers building NLP applications with spaCy
  • Researchers and practitioners who need ranked key phrases or extractive summaries from text

Documented uses

  • Extract and rank phrases from a text document.
  • Add TextRank-based processing to a spaCy application.
  • Create low-cost extractive summaries.
  • Help organize concepts inferred from unstructured text.
Before you choose

Requirements & limits

The public repository identifies an MIT license. The README documents installation through pip or Conda.

  • PyTextRank is a library to integrate into a Python application, rather than a hosted analysis service.
  • The documented setup uses spaCy and a separately installed language model.
  • The README says local builds are generally unnecessary except for contributors.
  • The project lists TextRank, PositionRank, Biased TextRank and TopicRank as supported algorithm families.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Add phrase extraction to a spaCy pipeline

Install PyTextRank and use the documented English-model example to inspect ranked phrases in a text document.

Before you start

  • Python 3 with pip
  • The spaCy package
  • The en_core_web_sm spaCy model

Steps

  1. Install PyTextRank with the command below.

    Documented command
    python3 -m pip install pytextrank
  2. Install the example language model with the command below.

    Documented command
    python3 -m spacy download en_core_web_sm
  3. In Python, import spacy and pytextrank, then load en_core_web_sm with spacy.load("en_core_web_sm").

  4. Add the component with nlp.add_pipe("textrank"), process text with nlp(text), and inspect doc._.phrases.

Expected outcome

The pipeline exposes phrases with text, rank, count and chunk information.

Guide limitations

  • The example uses the English en_core_web_sm model; the README says model choice depends on language and scale.
  • This guide follows the documented example and is not a report of an execution test.
Evidence and transparency

Sources & review

Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
DerwenAI/pytextrank · Official homepage title · retained in its source language