{"categories":[{"label":"Python Modules","url":"https://skillfed.io/packages/category/software-development-libraries-python-modules/19"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/9"},{"label":"Information Analysis","url":"https://skillfed.io/packages/category/scientific-engineering-information-analysis/3"},{"label":"Linguistic","url":"https://skillfed.io/packages/category/text-processing-linguistic/2"},{"label":"Visualization","url":"https://skillfed.io/packages/category/scientific-engineering-visualization"},{"label":"General","url":"https://skillfed.io/packages/category/text-processing-general"},{"label":"Indexing","url":"https://skillfed.io/packages/category/text-processing-indexing"},{"label":"Human Machine Interfaces","url":"https://skillfed.io/packages/category/scientific-engineering-human-machine-interfaces"}],"enrichment":{"capability":"PyTextRank implements graph-based TextRank and related algorithms as a spaCy pipeline extension to extract key phrases and perform extractive summarization on text documents.","skillfed_tags":["nlp","graph-algorithms","spacy-extension"],"use_cases":["Extract top-ranked phrases from research papers, articles, or documents for tagging or indexing","Perform low-cost extractive summarization by identifying key concepts without training a summarization model","Build knowledge graphs by extracting and ranking entities and relationships from unstructured text","Identify important keywords from customer feedback, reviews, or support tickets for categorization","Rank and filter candidate phrases for downstream NLP tasks like named entity linking or semantic search"],"what_it_does":"PyTextRank is a Python library that brings graph-based text analysis to spaCy by implementing the TextRank algorithm and related variants (PositionRank, Biased TextRank, TopicRank). It works as a spaCy pipeline component, meaning you load a language model, add the textrank pipe, and then process documents to extract ranked phrases and concepts. The library treats text as a graph of words and their relationships, then applies ranking algorithms to identify the most important phrases.\n\nThe main use cases are phrase extraction\u2014pulling the top-ranked keywords or key phrases from a document\u2014and low-cost extractive summarization, where you identify the most important sentences or concepts without training a neural model. It also helps convert unstructured text into more structured representations suitable for knowledge graphs. The package depends on spaCy for NLP processing, networkx for graph operations, scipy for numerical work, and several smaller utilities (graphviz, pygments, icecream, GitPython) for visualization and debugging.","worth_installing":"Yes. PyTextRank is actively maintained, has no known vulnerabilities, uses a permissive MIT license, and offers a straightforward way to add graph-based phrase extraction to spaCy workflows. Install friction is low and the library is well-documented. Choose it if you need phrase extraction or concept ranking without the overhead of training custom models."},"id":"pytextrank","links":{"html":"https://skillfed.io/packages/pytextrank","md":"https://skillfed.io/packages/pytextrank.md","pypi":"https://pypi.org/project/pytextrank/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2024-02-21","license_spdx":null,"license_treatment":"permissive","name":"pytextrank","python_support":"supports_current","summary":"Python implementation of TextRank as a spaCy pipeline extension, for graph-based natural language work plus related knowledge graph practices; used for for phrase extraction of text documents."},"popularity":{"monthly_downloads":117031,"position":12184,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"3.3.0"}