RRepoGEO

REPOGEO REPORT · LITE

nlpyang/BertSum

Default branch master · commit 05f8c634 · scanned 5/29/2026, 1:38:06 AM

GitHub: 1,506 stars · 416 forks

AI VISIBILITY SCORE
80 /100
Healthy
Category recall
2 / 2
Avg rank #3.5 when recommended
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 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 nlpyang/BertSum, 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
  • hightopics#1
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    extractive-summarization, bert, nlp, deep-learning, neural-networks, text-summarization, research-code, paper-implementation
  • highreadme#2
    Reposition the README's opening to emphasize research code

    Why:

    CURRENT
    # BertSum

**This code is for paper `Fine-tune BERT for Extractive Summarization`**(https://arxiv.org/pdf/1903.10318.pdf)
    COPY-PASTE FIX
    # BertSum: Code for Fine-tuning BERT for Extractive Summarization

**This repository provides the official code implementation for our research paper, `Fine-tune BERT for Extractive Summarization`**(https://arxiv.org/pdf/1903.10318.pdf). It's designed for NLP researchers and practitioners interested in advanced neural network models for summarization.
  • mediumhomepage#3
    Add the paper URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://arxiv.org/pdf/1903.10318.pdf

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
2 / 2
100% of queries surface nlpyang/BertSum
Avg rank
#3.5
Lower is better. #1 = top recommendation.
Share of voice
13%
Of all named tools, what % are you?
Top rival
Sumy
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Sumy · recommended 1×
  2. Gensim · recommended 1×
  3. NLTK · recommended 1×
  4. spaCy · recommended 1×
  5. PyTextRank · recommended 1×
  • CATEGORY QUERY
    Need a Python library for generating extractive text summaries from long articles.
    you: #6
    AI recommended (in order):
    1. Sumy
    2. Gensim
    3. NLTK
    4. spaCy
    5. PyTextRank
    6. BERTSum ← you
    7. Hugging Face Transformers
    Show full AI answer
  • CATEGORY QUERY
    What are effective neural network models for high-quality extractive document summarization?
    you: #1
    AI recommended (in order):
    1. BERTSUM ← you
    2. Longformer-Encoder-Decoder (LED)
    3. Longformer
    4. XLNet
    5. RoBERTa
    6. DistilBERT
    7. TextRank
    8. Sentence-BERT (SBERT)
    9. Universal Sentence Encoder (USE)
    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 nlpyang/BertSum?
    pass
    AI named nlpyang/BertSum explicitly

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

  • If a team adopts nlpyang/BertSum in production, what risks or prerequisites should they evaluate first?
    pass
    AI named nlpyang/BertSum 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 nlpyang/BertSum solve, and who is the primary audience?
    pass
    AI named nlpyang/BertSum explicitly

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

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nlpyang/BertSum — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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