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Enhance citation management and literature review guidelines
- Updated SKILL.md in citation management to include best practices for identifying seminal and high-impact papers, emphasizing citation count thresholds, venue quality tiers, and author reputation indicators. - Expanded literature review SKILL.md to prioritize high-impact papers, detailing citation metrics, journal tiers, and author reputation assessment. - Added comprehensive evaluation strategies for paper impact and quality in literature_search_strategies.md, including citation count significance and journal impact factor guidance. - Improved research lookup scripts to prioritize results based on citation count, venue prestige, and author reputation, enhancing the quality of research outputs.
This commit is contained in:
Vinayak Agarwal
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# CS Conference Writing Style Guide
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Comprehensive writing guide for ACL, EMNLP, NAACL (NLP), CHI, CSCW (HCI), SIGKDD, WWW, SIGIR (data mining/IR), and other major CS conferences.
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**Last Updated**: 2024
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---
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## Overview
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CS conferences span diverse subfields with distinct writing cultures. This guide covers NLP, HCI, and data mining/IR venues, each with unique expectations and evaluation criteria.
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---
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# Part 1: NLP Conferences (ACL, EMNLP, NAACL)
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## NLP Writing Philosophy
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> "Strong empirical results on standard benchmarks with insightful analysis."
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NLP papers balance empirical rigor with linguistic insight. Human evaluation is increasingly important alongside automatic metrics.
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## Audience and Tone
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### Target Reader
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- NLP researchers and computational linguists
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- Familiar with transformer architectures, standard benchmarks
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- Expect reproducible results and error analysis
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### Tone Characteristics
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| Characteristic | Description |
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|---------------|-------------|
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| **Task-focused** | Clear problem definition |
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| **Benchmark-oriented** | Standard datasets emphasized |
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| **Analysis-rich** | Error analysis, qualitative examples |
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| **Reproducible** | Full implementation details |
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## Abstract (NLP Style)
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### Structure
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- **Task/problem** (1 sentence)
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- **Limitation of prior work** (1 sentence)
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- **Your approach** (1-2 sentences)
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- **Results on benchmarks** (2 sentences)
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- **Analysis finding** (optional, 1 sentence)
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### Example Abstract
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```
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Coreference resolution remains challenging for pronouns with distant or
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ambiguous antecedents. Prior neural approaches struggle with these
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difficult cases due to limited context modeling. We introduce
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LongContext-Coref, a retrieval-augmented coreference model that
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dynamically retrieves relevant context from document history. On the
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OntoNotes 5.0 benchmark, LongContext-Coref achieves 83.4 F1, improving
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over the previous state-of-the-art by 1.2 points. On the challenging
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WinoBias dataset, we reduce gender bias by 34% while maintaining
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accuracy. Qualitative analysis reveals that our model successfully
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resolves pronouns requiring world knowledge, a known weakness of
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prior approaches.
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```
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## NLP Paper Structure
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```
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├── Introduction
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│ ├── Task motivation
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│ ├── Prior work limitations
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│ ├── Your contribution
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│ └── Contribution bullets
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├── Related Work
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├── Method
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│ ├── Problem formulation
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│ ├── Model architecture
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│ └── Training procedure
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├── Experiments
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│ ├── Datasets (with statistics)
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│ ├── Baselines
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│ ├── Main results
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│ ├── Analysis
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│ │ ├── Error analysis
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│ │ ├── Ablation study
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│ │ └── Qualitative examples
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│ └── Human evaluation (if applicable)
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├── Discussion / Limitations
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└── Conclusion
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```
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## NLP-Specific Requirements
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### Datasets
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- Use **standard benchmarks**: GLUE, SQuAD, CoNLL, OntoNotes
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- Report **dataset statistics**: train/dev/test sizes
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- **Data preprocessing**: Document all steps
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### Evaluation Metrics
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- **Task-appropriate metrics**: F1, BLEU, ROUGE, accuracy
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- **Statistical significance**: Paired bootstrap, p-values
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- **Multiple runs**: Report mean ± std across seeds
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### Human Evaluation
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Increasingly expected for generation tasks:
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- **Annotator details**: Number, qualifications, agreement
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- **Evaluation protocol**: Guidelines, interface, payment
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- **Inter-annotator agreement**: Cohen's κ or Krippendorff's α
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### Example Human Evaluation Table
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```
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Table 3: Human Evaluation Results (100 samples, 3 annotators)
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─────────────────────────────────────────────────────────────
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Method | Fluency | Coherence | Factuality | Overall
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─────────────────────────────────────────────────────────────
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Baseline | 3.8 | 3.2 | 3.5 | 3.5
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GPT-3.5 | 4.2 | 4.0 | 3.7 | 4.0
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Our Method | 4.4 | 4.3 | 4.1 | 4.3
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─────────────────────────────────────────────────────────────
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Inter-annotator κ = 0.72. Scale: 1-5 (higher is better).
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```
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## ACL-Specific Notes
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- **ARR (ACL Rolling Review)**: Shared review system across ACL venues
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- **Responsible NLP checklist**: Ethics, limitations, risks
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- **Long (8 pages) vs. Short (4 pages)**: Different expectations
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- **Findings papers**: Lower-tier acceptance track
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---
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# Part 2: HCI Conferences (CHI, CSCW, UIST)
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## HCI Writing Philosophy
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> "Technology in service of humans—understand users first, then design and evaluate."
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HCI papers are fundamentally **user-centered**. Technology novelty alone is insufficient; understanding human needs and demonstrating user benefit is essential.
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## Audience and Tone
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### Target Reader
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- HCI researchers and practitioners
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- UX designers and product developers
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- Interdisciplinary (CS, psychology, design, social science)
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### Tone Characteristics
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| Characteristic | Description |
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|---------------|-------------|
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| **User-centered** | Focus on people, not technology |
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| **Design-informed** | Grounded in design thinking |
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| **Empirical** | User studies provide evidence |
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| **Reflective** | Consider broader implications |
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## HCI Abstract
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### Focus on Users and Impact
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```
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Video calling has become essential for remote collaboration, yet
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current interfaces poorly support the peripheral awareness that makes
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in-person work effective. Through formative interviews with 24 remote
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workers, we identified three key challenges: difficulty gauging
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colleague availability, lack of ambient presence cues, and interruption
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anxiety. We designed AmbientOffice, a peripheral display system that
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conveys teammate presence through subtle ambient visualizations. In a
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two-week deployment study with 18 participants across three distributed
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teams, AmbientOffice increased spontaneous collaboration by 40% and
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reduced perceived isolation (p<0.01). Participants valued the system's
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non-intrusive nature and reported feeling more connected to remote
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colleagues. We discuss implications for designing ambient awareness
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systems and the tension between visibility and privacy in remote work.
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```
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## HCI Paper Structure
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### Research Through Design / Systems Papers
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```
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├── Introduction
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│ ├── Problem in human terms
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│ ├── Why technology can help
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│ └── Contribution summary
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├── Related Work
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│ ├── Domain background
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│ ├── Prior systems
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│ └── Theoretical frameworks
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├── Formative Work (often)
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│ ├── Interviews / observations
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│ └── Design requirements
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├── System Design
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│ ├── Design rationale
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│ ├── Implementation
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│ └── Interface walkthrough
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├── Evaluation
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│ ├── Study design
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│ ├── Participants
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│ ├── Procedure
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│ ├── Findings (quant + qual)
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│ └── Limitations
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├── Discussion
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│ ├── Design implications
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│ ├── Generalizability
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│ └── Future work
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└── Conclusion
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```
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### Qualitative / Interview Studies
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```
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├── Introduction
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├── Related Work
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├── Methods
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│ ├── Participants
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│ ├── Procedure
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│ ├── Data collection
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│ └── Analysis method (thematic, grounded theory, etc.)
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├── Findings
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│ ├── Theme 1 (with quotes)
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│ ├── Theme 2 (with quotes)
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│ └── Theme 3 (with quotes)
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├── Discussion
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│ ├── Implications for design
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│ ├── Implications for research
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│ └── Limitations
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└── Conclusion
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```
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## HCI-Specific Requirements
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### Participant Reporting
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- **Demographics**: Age, gender, relevant experience
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- **Recruitment**: How and where recruited
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- **Compensation**: Payment amount and type
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- **IRB approval**: Ethics board statement
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### Quotes in Findings
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Use direct quotes to ground findings:
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```
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Participants valued the ambient nature of the display. As P7 described:
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"It's like having a window to my teammate's office. I don't need to
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actively check it, but I know they're there." This passive awareness
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reduced the barrier to initiating contact.
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```
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### Design Implications Section
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Translate findings into actionable guidance:
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```
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**Implication 1: Support peripheral awareness without demanding attention.**
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Ambient displays should be visible in peripheral vision but not require
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active monitoring. Designers should consider calm technology principles.
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**Implication 2: Balance visibility with privacy.**
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Users want to share presence but fear surveillance. Systems should
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provide granular controls and make visibility mutual.
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```
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## CHI-Specific Notes
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- **Contribution types**: Empirical, artifact, methodological, theoretical
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- **ACM format**: `acmart` document class with `sigchi` option
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- **Accessibility**: Alt text, inclusive language expected
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- **Contribution statement**: Required per-author contributions
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---
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# Part 3: Data Mining & IR (SIGKDD, WWW, SIGIR)
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## Data Mining Writing Philosophy
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> "Scalable methods for real-world data with demonstrated practical impact."
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Data mining papers emphasize **scalability**, **real-world applicability**, and **solid experimental methodology**.
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## Audience and Tone
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### Target Reader
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- Data scientists and ML engineers
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- Industry researchers
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- Applied ML practitioners
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### Tone Characteristics
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| Characteristic | Description |
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|---------------|-------------|
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| **Scalable** | Handle large datasets |
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| **Practical** | Real-world applications |
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| **Reproducible** | Datasets and code shared |
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| **Industrial** | Industry datasets valued |
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## KDD Abstract
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### Emphasize Scale and Application
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```
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Fraud detection in e-commerce requires processing millions of
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transactions in real-time while adapting to evolving attack patterns.
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We present FraudShield, a graph neural network framework for real-time
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fraud detection that scales to billion-edge transaction graphs. Unlike
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prior methods that require full graph access, FraudShield uses
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incremental updates with O(1) inference cost per transaction. On a
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proprietary dataset of 2.3 billion transactions from a major e-commerce
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platform, FraudShield achieves 94.2% precision at 80% recall,
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outperforming production baselines by 12%. The system has been deployed
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at [Company], processing 50K transactions per second and preventing
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an estimated $400M in annual fraud losses. We release an anonymized
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benchmark dataset and code.
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```
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## KDD Paper Structure
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```
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├── Introduction
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│ ├── Problem and impact
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│ ├── Technical challenges
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│ ├── Your approach
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│ └── Contributions
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├── Related Work
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├── Preliminaries
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│ ├── Problem definition
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│ └── Notation
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├── Method
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│ ├── Overview
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│ ├── Technical components
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│ └── Complexity analysis
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├── Experiments
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│ ├── Datasets (with scale statistics)
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│ ├── Baselines
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│ ├── Main results
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│ ├── Scalability experiments
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│ ├── Ablation study
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│ └── Case study / deployment
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└── Conclusion
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```
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## KDD-Specific Requirements
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### Scalability
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- **Dataset sizes**: Report number of nodes, edges, samples
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- **Runtime analysis**: Wall-clock time comparisons
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- **Complexity**: Time and space complexity stated
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- **Scaling experiments**: Show performance vs. data size
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### Industrial Deployment
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- **Case studies**: Real-world deployment stories
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- **A/B tests**: Online evaluation results (if applicable)
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- **Production metrics**: Business impact (if shareable)
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### Example Scalability Table
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```
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Table 4: Scalability Comparison (runtime in seconds)
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──────────────────────────────────────────────────────
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Dataset | Nodes | Edges | GCN | GraphSAGE | Ours
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──────────────────────────────────────────────────────
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Cora | 2.7K | 5.4K | 0.3 | 0.2 | 0.1
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Citeseer | 3.3K | 4.7K | 0.4 | 0.3 | 0.1
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PubMed | 19.7K | 44.3K | 1.2 | 0.8 | 0.3
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ogbn-arxiv | 169K | 1.17M | 8.4 | 4.2 | 1.6
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ogbn-papers | 111M | 1.6B | OOM | OOM | 42.3
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──────────────────────────────────────────────────────
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```
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---
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# Part 4: Common Elements Across CS Venues
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## Writing Quality
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### Clarity
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- **One idea per sentence**
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- **Define terms before use**
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- **Use consistent notation**
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### Precision
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- **Exact numbers**: "23.4%" not "about 20%"
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- **Clear claims**: Avoid hedging unless necessary
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- **Specific comparisons**: Name the baseline
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## Contribution Bullets
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Used across all CS venues:
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```
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Our contributions are:
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• We identify [problem/insight]
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• We propose [method name] that [key innovation]
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• We demonstrate [results] on [benchmarks]
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• We release [code/data] at [URL]
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```
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## Reproducibility Standards
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All CS venues increasingly expect:
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- **Code availability**: GitHub link (anonymous for review)
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- **Data availability**: Public datasets or release plans
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- **Full hyperparameters**: Training details complete
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- **Random seeds**: Exact values for reproduction
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## Ethics and Broader Impact
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### NLP (ACL/EMNLP)
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- **Limitations section**: Required
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- **Responsible NLP checklist**: Ethical considerations
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- **Bias analysis**: For models affecting people
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### HCI (CHI)
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- **IRB/Ethics approval**: Required for human subjects
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- **Informed consent**: Procedure described
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- **Privacy considerations**: Data handling
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### KDD/WWW
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- **Societal impact**: Consider misuse potential
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- **Privacy preservation**: For sensitive data
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- **Fairness analysis**: When applicable
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---
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## Venue Comparison Table
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| Aspect | ACL/EMNLP | CHI | KDD/WWW | SIGIR |
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|--------|-----------|-----|---------|-------|
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| **Focus** | NLP tasks | User studies | Scalable ML | IR/search |
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| **Evaluation** | Benchmarks + human | User studies | Large-scale exp | Datasets |
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| **Theory weight** | Moderate | Low | Moderate | Moderate |
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| **Industry value** | High | Medium | Very high | High |
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| **Page limit** | 8 long / 4 short | 10 + refs | 9 + refs | 10 + refs |
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| **Review style** | ARR | Direct | Direct | Direct |
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---
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## Pre-Submission Checklist
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### All CS Venues
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- [ ] Clear contribution statement
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- [ ] Strong baselines
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- [ ] Reproducibility information complete
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- [ ] Correct venue template
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- [ ] Anonymized (if double-blind)
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### NLP-Specific
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- [ ] Standard benchmark results
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- [ ] Error analysis included
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- [ ] Human evaluation (for generation)
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- [ ] Responsible NLP checklist
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### HCI-Specific
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- [ ] IRB approval stated
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- [ ] Participant demographics
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- [ ] Direct quotes in findings
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- [ ] Design implications
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### Data Mining-Specific
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- [ ] Scalability experiments
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- [ ] Dataset size statistics
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- [ ] Runtime comparisons
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- [ ] Complexity analysis
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---
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## See Also
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- `venue_writing_styles.md` - Master style overview
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- `ml_conference_style.md` - NeurIPS/ICML style guide
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- `conferences_formatting.md` - Technical formatting requirements
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- `reviewer_expectations.md` - What CS reviewers seek
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Reference in New Issue
Block a user