REPOGEO REPORT · LITE

yaserkl/RLSeq2Seq

Default branch master · commit add42c4b · scanned 6/8/2026, 2:17:37 AM

GitHub: 767 stars · 160 forks

AI VISIBILITY SCORE

40 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 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 yaserkl/RLSeq2Seq, 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

Rephrase the 'no longer actively maintained' note to highlight research value

Why:

CURRENT

NOTE: This code is no longer actively maintained.

This repository contains the code developed in TensorFlow_ for the following paper:
| `Deep Reinforcement Learning For Sequence to Sequence Models`_,
| by: `Yaser Keneshloo`_, `Tian Shi`_, `Naren Ramakrishnan`_, and `Chandan K. Reddy`_

COPY-PASTE FIX

This repository provides the TensorFlow implementation for 'Deep Reinforcement Learning For Sequence to Sequence Models'. While no longer actively maintained, it remains a valuable resource for researchers and practitioners interested in replicating results or studying the application of reinforcement learning to sequence-to-sequence models for tasks like abstractive text summarization.

mediumreadme#2
Enhance README's H1 to clearly state the project's specific application
Why:
CURRENT
```
RLSeq2Seq
```
COPY-PASTE FIX
```
RLSeq2Seq: Deep Reinforcement Learning for Abstractive Text Summarization and NLP Sequence Generation
```

lowtopics#3

Add 'tensorflow' to repository topics

Why:

CURRENT

abstractive-text-summarization, actor-critic, nlp, pointer-generator, policy-gradient, reinforcement-learning

COPY-PASTE FIX

abstractive-text-summarization, actor-critic, nlp, pointer-generator, policy-gradient, reinforcement-learning, tensorflow

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 yaserkl/RLSeq2Seq

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

huggingface/transformers

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

huggingface/transformers · recommended 2×
pytorch/pytorch · recommended 2×
tensorflow/tensorflow · recommended 2×
ray-project/ray · recommended 2×
DLR-RM/stable-baselines3 · recommended 1×

CATEGORY QUERY
How can I apply deep reinforcement learning for abstractive text summarization tasks?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. PyTorch (pytorch/pytorch)
3. TensorFlow (tensorflow/tensorflow)
4. RLlib (Ray) (ray-project/ray)
5. Stable Baselines3 (DLR-RM/stable-baselines3)
6. TensorFlow Agents (TF-Agents) (tensorflow/agents)
7. rouge-score (google-research/rouge-score)
8. files2rouge (pltrdy/files2rouge)
9. bert_score (Tiiiger/bert_score)
10. NLTK (Natural Language Toolkit) (nltk/nltk)
AI recommended 10 alternatives but never named yaserkl/RLSeq2Seq. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What frameworks help implement actor-critic or policy gradient methods for NLP sequence generation?
you: not recommended
AI recommended (in order):
1. Hugging Face Transformers (huggingface/transformers)
2. Hugging Face Accelerate (huggingface/accelerate)
3. PyTorch (pytorch/pytorch)
4. PyTorch-Lightning (Lightning-AI/lightning)
5. TensorFlow (tensorflow/tensorflow)
6. Keras (keras-team/keras)
7. RLlib (ray-project/ray)
8. DeepMind's Acme (deepmind/acme)
9. Catalyst (catalyst-team/catalyst)
AI recommended 9 alternatives but never named yaserkl/RLSeq2Seq. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
pass
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 yaserkl/RLSeq2Seq?
pass
AI named yaserkl/RLSeq2Seq explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts yaserkl/RLSeq2Seq in production, what risks or prerequisites should they evaluate first?
pass
AI named yaserkl/RLSeq2Seq 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 yaserkl/RLSeq2Seq solve, and who is the primary audience?
pass
AI named yaserkl/RLSeq2Seq explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

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Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite