Module: Title Optimization

Trigger: title, 标题, title optimization, create title, improve title

Purpose: Generate and optimize paper titles following IEEE/ACM/Springer/NeurIPS best practices.

Usage Examples:

Generate title from content:

uv run python -B scripts/optimize_title.py main.tex --generate
# Analyzes abstract/introduction to propose 3-5 title candidates

Optimize existing title:

uv run python -B scripts/optimize_title.py main.tex --optimize
# Analyzes current title and provides improvement suggestions

Check title quality:

uv run python -B scripts/optimize_title.py main.tex --check
# Evaluates title against best practices (score 0-100)

Title Quality Criteria (Based on IEEE Author Center & Top Venues):

Criterion	Weight	Description
Conciseness	25%	Remove "A Study of", "Research on", "Novel", "New", "Improved"
Searchability	30%	Key terms (Method + Problem) in first 65 characters
Length	15%	Optimal: 10-15 words; Acceptable: 8-20 words
Specificity	20%	Concrete method/problem names, not vague terms
Jargon-Free	10%	Avoid obscure abbreviations (except AI, LSTM, DNA, etc.)

Title Generation Workflow:

Step 1: Content Analysis Extract from abstract/introduction:

• Problem: What challenge is addressed?
• Method: What approach is proposed?
• Domain: What application area?
• Key Result: What is the main achievement? (optional)

Step 2: Keyword Extraction Identify 3-5 core keywords:

• Method keywords: "Transformer", "Graph Neural Network", "Reinforcement Learning"
• Problem keywords: "Time Series Forecasting", "Fault Detection", "Image Segmentation"
• Domain keywords: "Industrial Control", "Medical Imaging", "Autonomous Driving"

Step 3: Title Template Selection Common patterns for top venues:

Pattern	Example	Use Case
Method for Problem	"Transformer-Based Approach for Time Series Forecasting"	General research
Method: Problem in Domain	"Graph Neural Networks: Fault Detection in Industrial Systems"	Domain-specific
Problem via Method	"Time Series Forecasting via Attention Mechanisms"	Method-focused
Method + Key Feature	"Lightweight Transformer for Real-Time Object Detection"	Performance-focused

Step 4: Title Candidates Generation Generate 3-5 candidates with different emphasis:

1. Method-focused
2. Problem-focused
3. Application-focused
4. Balanced (recommended)
5. Concise variant

Step 5: Quality Scoring Each candidate receives:

• Overall score (0-100)
• Breakdown by criterion
• Specific improvement suggestions

Title Optimization Rules:

Remove Ineffective Words:

Avoid	Reason
A Study of	Redundant (all papers are studies)
Research on	Redundant (all papers are research)
Novel / New	Implied by publication
Improved / Enhanced	Vague without specifics
Based on	Often unnecessary
Using / Utilizing	Can be replaced with prepositions

Preferred Structures:

Good: "Transformer for Time Series Forecasting in Industrial Control"
Bad:  "A Novel Study on Improved Time Series Forecasting Using Transformers"

Good: "Graph Neural Networks for Fault Detection"
Bad:  "Research on Novel Fault Detection Based on GNNs"

Good: "Attention-Based LSTM for Multivariate Time Series Prediction"
Bad:  "An Improved LSTM Model Using Attention Mechanism for Prediction"

Keyword Placement Strategy:

• First 65 characters: Most important keywords (Method + Problem)
• Avoid starting with: Articles (A, An, The), prepositions (On, In, For)
• Prioritize: Nouns and technical terms over verbs and adjectives

Abbreviation Guidelines:

Acceptable	Avoid in Title
AI, ML, DL	Obscure domain-specific acronyms
LSTM, GRU, CNN	Chemical formulas (unless very common)
IoT, 5G, GPS	Lab-specific abbreviations
DNA, RNA, MRI	Non-standard method names

Venue-Specific Adjustments:

IEEE Transactions:

• Avoid formulas with subscripts (except simple ones like "Nd–Fe–B")
• Use title case (capitalize major words)
• Typical length: 10-15 words
• Example: "Deep Learning for Predictive Maintenance in Smart Manufacturing"

ACM Conferences:

• More flexible with creative titles
• Can use colons for subtitles
• Typical length: 8-12 words
• Example: "AttentionFlow: Visualizing Attention Mechanisms in Neural Networks"

Springer Journals:

• Prefer descriptive over creative
• Can be slightly longer (up to 20 words)
• Example: "A Comprehensive Framework for Real-Time Anomaly Detection in Industrial IoT Systems"

NeurIPS/ICML:

• Concise and impactful (8-12 words)
• Method name often prominent
• Example: "Transformers Learn In-Context by Gradient Descent"

Output Format:

% ============================================================
% TITLE OPTIMIZATION REPORT
% ============================================================
% Current Title: "A Novel Study on Time Series Forecasting Using Deep Learning"
% Quality Score: 45/100
%
% Issues Detected:
% 1. [Critical] Contains "Novel Study" (remove ineffective words)
% 2. [Major] Vague method description ("Deep Learning" too broad)
% 3. [Minor] Length acceptable (9 words) but could be more specific
%
% Recommended Titles (Ranked):
%
% 1. "Transformer-Based Time Series Forecasting for Industrial Control" [Score: 92/100]
%    - Concise: ✅ (8 words)
%    - Searchable: ✅ (Method + Problem in first 50 chars)
%    - Specific: ✅ (Transformer, not just "Deep Learning")
%    - Domain: ✅ (Industrial Control)
%
% 2. "Attention Mechanisms for Multivariate Time Series Prediction" [Score: 88/100]
%    - Concise: ✅ (7 words)
%    - Searchable: ✅ (Key terms upfront)
%    - Specific: ✅ (Attention, Multivariate)
%    - Note: Consider adding domain if space allows
%
% 3. "Deep Learning Approach to Time Series Forecasting in Smart Manufacturing" [Score: 78/100]
%    - Concise: ⚠️ (10 words, acceptable)
%    - Searchable: ✅
%    - Specific: ⚠️ ("Deep Learning" still broad)
%    - Domain: ✅ (Smart Manufacturing)
%
% Keyword Analysis:
% - Primary: Transformer, Time Series, Forecasting
% - Secondary: Industrial Control, Attention, LSTM
% - Searchability: "Transformer Time Series" appears in 1,234 papers (good balance)
%
% Suggested LaTeX Update:
% \title{Transformer-Based Time Series Forecasting for Industrial Control}
% ============================================================

Interactive Mode (Recommended):

uv run python -B scripts/optimize_title.py main.tex --interactive
# Step-by-step guided title creation with user input

Batch Mode (For multiple papers):

uv run python -B scripts/optimize_title.py "papers/*.tex" --batch --output title_report.json

Title A/B Testing (Optional):

uv run python -B scripts/optimize_title.py main.tex --compare "Title A" "Title B" "Title C"
# Compares multiple title candidates with detailed scoring

Best Practices Summary:

1. Start with keywords: Put Method + Problem in first 10 words
2. Be specific: "Transformer" > "Deep Learning" > "Machine Learning"
3. Remove fluff: Delete "Novel", "Study", "Research", "Based on"
4. Check length: Aim for 10-15 words (English)
5. Test searchability: Would you find this paper with these keywords?
6. Avoid jargon: Unless it's widely recognized (AI, LSTM, CNN)
7. Match venue style: IEEE (descriptive), ACM (creative), NeurIPS (concise)

Reference: IEEE Author Center, Royal Society Blog