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): "yet", "unclear", "understudied", "growing", "challenge", "blank", "despite", "little research"

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): "This article aims to", "This research discusses", "This article proposes", "Research purpose", "For this reason we", "This work", "This article studies"

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): "adopt", "method", "dataset", "sample", "model", "algorithm", "framework", "experiment", "training", "evaluation"

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): "results show", "reach", "better than", "accuracy", "improve", "reduce", "find", "significant", 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): "research findings indicate", "provide for", "contribute to", "have significance for", "can be used for", "promote", "contribute"

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

Dissertation abstract skeleton (thesis model)

The above five-factor model is of conference/journal small paper caliber. Abstracts for Chinese degree theses (especially engineering PhDs) follow a different set of Skeleton Structure: Not the five sections of Background/Objective/Methods/Results/Conclusion, but "Object Positioning → Pain Points → The starting sentence ends with a colon → the numbered working section → the optional closing section." analyze_abstract.py of --model thesis is the default, Diagnose this skeleton; --model five Keep the above five-element model as a backup (this skill only serves dissertations, and the five-element model is suitable for Ph.D. abstracts will systematically misreport, for example, Results will have no value and will be MISSING, while compliant Ph.D. abstracts will always be closed).

Skeleton sequence (macro)

① 对象定位首句："X 是……" / "X 产生于……"（研究对象为主语，非方法开头）
② 痛点/挑战段："然而，……难以/挑战/瓶颈……"
③ 总起句 + 冒号收束："本文主要研究工作/创新点如下："
④ 编号工作段 (1)(2)(3)…：每段"针对……问题，提出/建立……，实验/应用表明……"
⑤ 可选收尾段：综述成果/工程应用（"优化/工程应用"类论文常见，非必需）

Number of paragraphs = background paragraph (1~2) + working paragraph (number) + optional closing paragraph.

Relationship with the five-factor model

Dimensions	Five-element model (--model five)	Dissertation skeleton (--model thesis, default)
Applicable	Conference/journal papers	Chinese doctoral/master's thesis
Main Body	Background/Objective/Methods/Results/Conclusion Five Sections	Numbered Work Sections (1)(2)(3)…
Numeric value	Results No value judgment VAGUE/MISSING	Numeric value is optional (4/5 qualitative closing compliance), only check the robust statement when it appears
Word Count	EN 150~250 words / ZH 200~300 words	Alignment check_spec Yanshan Constant: PhD 900~1200 words / Master 500~650 words

The word count threshold is switched by --degree {doctor,master} (default doctor), and --max-chars can override the upper bound.

T-* Grading rule table

The diagnostic item corresponds to the research abstract-patterns.md number; ★ mark (≥4/5) is the default alarm, and the 2~3/5 rule is only Info:

Check code	Content	Level	Traceability
T-OPEN	The first sentence starts with the research object as the subject and not the method	Warning	★A1 5/5
T-PAIN	There are pain points/challenge sentences (difficult/challenge/yet/bottleneck)	Warning	★A2 5/5
T-LEAD	There is a starting sentence before the numbered paragraph and it ends with ":"	Warning	★A4 5/5
T-ENUM	The main body is (1)(2)... numbered working section, and the number of sections is consistent with the number	Warning	★A5 5/5, D4
T-VERIFY	Verification method roll call (simulation/actual measurement/production data/field application), non-empty "verification valid"	Warning	★C2 5/5
T-ABBR	The full name in Chinese and English is defined when the abbreviation first appears	Warning	★E3 5/5
T-INNOV	The occurrence of innovative expressions (innovation/first time/new method or the numbered work section itself)	Warning	web A3 school regulations
T-TOC-STYLE	Non-table of contents abstract / background description is not too long	Warning	web A10 soft
T-PROB	Each work section begins with a question-oriented phrase (only if <50% of the entire article is reported)	Info	★B1
T-VERB	Method verb specification set (propose/establish/design/construct/research/adopt)	Info	★B4
T-NUM-HEDGE	Numeric indicators with "approximate/above/range" robust expression (check only if numerical values are available)	Info	C3 2/2
T-KW-FIRST	First keyword ≈ research object/process name	Info	★D2
T-VOICE	Only search "I/We/Author"; "This article/This paper" is legal	Info	web A6

Chinese-English abstract consistency (--bilingual)

When adding --bilingual to thesis mode, an additional comparison is made between English Abstract and Chinese abstract:

Check code	Content	Level	Traceability
B-ORD	First/Second/Then/Finally ↔ First/Second/Then/Finally Number and order alignment	Warning	★F3 5/5
B-NUM	Chinese and English numerical token sets are consistent	Error (inconsistent values are a flaw)	★F1; web A9
B-ENUM	The number of numbered working segments is the same	Warning	★F1
B-LEN	English abstract is missing/too short	Warning	web A9
B-SEM	Sentence-by-sentence/element-by-element semantic correspondence ([LLM] lane, reported to control prompt words)	—	★F1

Tense/Voice (★F2 English summary method sentences are usually present tense passive) Not implemented here: The deai module already has English summary area gated Tense detection (tense-guide-zh.md + deai_check), --bilingual report endnote guide deai, Avoid dual implementation drift (deai trace does not flow into this module).

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: ...]