letiantian/TextRank4ZH
:deciduous_tree:从中文文本中自动提取关键词和摘要 observed · 2026-08-28
Health v2 · maintenance only
41/100
- Activity 20
- Release rhythm 35
- Longevity 100
Flags: no_releases
How is this computed?
round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.
- gap_med: n/a
- age_days: 4293
- days_rel: n/a
- days_push: 484
- n_releases_24m: 0
Adoption not part of the score
3393 stars · 834 forks observed · 2026-08-28
What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded
A Python implementation of the TextRank algorithm tailored for Chinese text, extracting keywords, key phrases, and extractive summaries using PageRank over word and sentence graphs. It depends on jieba for segmentation, numpy, and networkx.
Use cases
- extract keywords from Chinese text
- generate summaries of Chinese articles
- find key phrases in Chinese documents
- rank important sentences in Chinese text
- implement TextRank for Chinese NLP
- summarize Chinese news articles
When to choose
- you need keyword or extractive summarization for Chinese-language text
- you want a lightweight, pure-Python TextRank with jieba segmentation
- you need key phrase extraction that joins adjacent keywords
When to avoid
- you need extractive summarization for non-Chinese languages
- you need abstractive or neural summarization quality
- you need actively maintained tooling with modern packaging
Facets
library · maturity maintenance
nlp parser python textrank keyword-extraction text-summarization pagerank chinese-nlp natural-language-processing chinese-text
1 source
- readme: https://github.com/letiantian/TextRank4ZH · fetched 2026-08-28 · 4af0eef5afce
Member repositories
| Repository | Role | Health v2 |
|---|---|---|
| letiantian/TextRank4ZH | main | 41 |
For agents
markdown · JSON · MCP: product_card(name="letiantian/TextRank4ZH")
Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem