RRepoGEO

REPOGEO REPORT · LITE

icoxfog417/awesome-text-summarization

Default branch master · commit a55f6d60 · scanned 5/28/2026, 4:33:08 AM

GitHub: 1,313 stars · 204 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface icoxfog417/awesome-text-summarization, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Reposition README H1 and About description to clarify repo type

    Why:

    CURRENT
    The guide to tackle with the Text Summarization.
    COPY-PASTE FIX
    An awesome list and comprehensive guide to resources for Text Summarization.
  • mediumhomepage#2
    Add a homepage URL to the repository's About section

    Why:

    COPY-PASTE FIX
    https://[YOUR_PROJECT_HOMEPAGE_URL_HERE]
  • mediumtopics#3
    Refine topics for specificity

    Why:

    CURRENT
    machine-learning, natural-language-processing, python, text-summarization
    COPY-PASTE FIX
    machine-learning, natural-language-processing, python, text-summarization, awesome-list, resource-collection, guide

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface icoxfog417/awesome-text-summarization
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
explosion/spaCy
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. explosion/spaCy · recommended 2×
  2. huggingface/transformers · recommended 2×
  3. nltk/nltk · recommended 2×
  4. pymupdf/PyMuPDF · recommended 1×
  5. py-pdf/PyPDF2 · recommended 1×
  • CATEGORY QUERY
    What are effective Python tools for automatically extracting key information from large documents?
    you: not recommended
    AI recommended (in order):
    1. spaCy (explosion/spaCy)
    2. Hugging Face Transformers (huggingface/transformers)
    3. NLTK (nltk/nltk)
    4. PyMuPDF (pymupdf/PyMuPDF)
    5. PyPDF2 (py-pdf/PyPDF2)
    6. Tesseract OCR (tesseract-ocr/tesseract)
    7. pytesseract (madmaze/pytesseract)
    8. Scrapy (scrapy/scrapy)

    AI recommended 8 alternatives but never named icoxfog417/awesome-text-summarization. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How can I implement abstractive or extractive text summarization techniques in my project?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers (huggingface/transformers)
    2. Gensim (piskvorky/gensim)
    3. NLTK (nltk/nltk)
    4. SpaCy (explosion/spaCy)
    5. Sumy (miso-belica/sumy)
    6. OpenNMT (OpenNMT/OpenNMT-py)
    7. Google Cloud AI Platform
    8. AWS SageMaker
    9. Azure Machine Learning

    AI recommended 9 alternatives but never named icoxfog417/awesome-text-summarization. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of icoxfog417/awesome-text-summarization?
    pass
    AI named icoxfog417/awesome-text-summarization explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts icoxfog417/awesome-text-summarization in production, what risks or prerequisites should they evaluate first?
    pass
    AI named icoxfog417/awesome-text-summarization explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo icoxfog417/awesome-text-summarization solve, and who is the primary audience?
    pass
    AI did not name icoxfog417/awesome-text-summarization — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of icoxfog417/awesome-text-summarization. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite