Abstract Structure Guide

An effective academic abstract contains five structural elements that together tell a complete research story. This guide defines each element, how to detect it, and what makes it strong or weak.

Five-Element Model

1. Background

Purpose: Establish the research context — the real-world problem, knowledge gap, or motivation.

Detection markers (EN): "however", "remains unclear", "limited research", "growing interest", "challenge", "gap", "despite", "little is known", "increasingly important"

Detection markers (ZH): "然而", "尚不清楚", "研究不足", "日益增长", "挑战", "空白", "尽管", "鲜有研究"

Quality criteria: Moves from broad context to specific gap in 1-2 sentences. A vague background restates the field name without identifying a gap.

2. Objective

Purpose: State what this specific study aims to answer or accomplish.

Detection markers (EN): "this study aims", "we investigate", "the purpose of", "this paper presents", "we propose", "our goal", "in this work", "we address", "this research examines"

Detection markers (ZH): "本文旨在", "本研究探讨", "本文提出", "研究目的", "为此我们", "本工作", "本文研究"

Quality criteria: Specific and falsifiable. A vague objective says "we study X" without specifying what aspect or what question about X.

3. Methods

Purpose: Describe the approach, data, tools, or analytical framework used.

Detection markers (EN): "we propose", "using", "dataset", "participants", "method", "approach", "framework", "model", "algorithm", "collected", "trained", "evaluated", "sample", "experiment"

Detection markers (ZH): "采用", "方法", "数据集", "样本", "模型", "算法", "框架", "实验", "训练", "评估"

Quality criteria: Names the specific technique, data source, or experimental setup. Missing methods make the abstract feel like an opinion piece.

4. Results

Purpose: Report the key findings with concrete data.

Detection markers (EN): "results show", "achieved", "outperforms", "accuracy", "improved", "reduced", "found that", "demonstrates", "significant", numbers, percentages, p-values

Detection markers (ZH): "结果表明", "达到", "优于", "准确率", "提高", "降低", "发现", "显著", numbers

Quality criteria: Must contain at least one quantitative finding (number, percentage, ratio, or comparative statement with magnitude). A results section without numbers is classified as VAGUE.

5. Conclusion / Significance

Purpose: State the contribution, implications, or practical value of the findings.

Detection markers (EN): "our findings suggest", "contributes to", "implications", "demonstrates that", "can be used", "enables", "provides", "advances", "potential"

Detection markers (ZH): "研究发现表明", "为...提供", "有助于", "具有...意义", "可用于", "推动", "贡献"

Quality criteria: Goes beyond restating results — connects findings to the broader field or practice. A hollow conclusion just repeats the results in different words.

Common Defect Patterns

Defect	Description	Typical fix
Missing background	Jumps straight to "We propose..."	Add 1 sentence on the problem context
Vague objective	"We study deep learning for NLP"	Specify: "We investigate whether... improves..."
No methods	Describes results without explaining how	Add the core technique and data source
Data-free results	"Our method performs well"	Add a key metric: "achieves 94.2% F1"
Echo conclusion	Restates results verbatim	Add implication: "enabling real-time..."

Word Count Guidelines

Context	Language	Range
Default (no venue specified)	English	150–250 words
Default (no venue specified)	Chinese	200–300 characters
IEEE conference	English	150–200 words
ACM conference	English	150–250 words
NeurIPS/ICML	English	≤ 200 words (strict)
Chinese thesis (GB/T)	Chinese	300–500 characters

Venue-specific limits override defaults. Check catalog.md for exact requirements.

Diagnostic Output Format

The analyzer outputs a per-element diagnosis:

Background:  ✅ PRESENT  — "Despite growing interest in X, the impact of Y remains unclear."
Objective:   ⚠️ VAGUE    — "This paper studies X." → Suggestion: specify the research question
Methods:     ✅ PRESENT  — "We propose a framework based on Z, evaluated on dataset W."
Results:     ❌ MISSING  — No quantitative findings detected → Add key metrics
Conclusion:  ⚠️ VAGUE    — Restates results without implications → Add practical significance

Constraints

• Never alter the author's core claims or fabricate data
• Never add results or conclusions not present in the original text
• Preserve all citations, labels, and math environments
• Mark all modifications with brackets: [ADDED: ...] or [REVISED: ...]