SCROLL Feature
Abstract Matching
Quickly discover related undergraduate theses by comparing your abstract to thousands of existing works in the repository. SCROLL uses semantic similarity, not just exact keywords, to suggest relevant studies.
How Abstract Matching Works in SCROLL
When you paste or upload your abstract, SCROLL analyzes the text and converts it into a semantic representation. Instead of only checking for matching words, the system looks at meaning and context, then compares it with abstracts already stored in the repository.
- Enter or paste your abstract into the Abstract Matching tool.
- SCROLL processes the text using Natural Language Processing (NLP) techniques.
- The system finds theses with similar topics, methods, or research gaps.
- You get a ranked list of recommended papers to review or cite.
Why Abstract Matching Is Helpful
Stronger RRL
Build a more complete Related Literature and Studies section by easily finding similar local works and identifying what has already been done.
Avoid Topic Duplication
Quickly see if your proposed title is too close to existing theses and adjust the scope or variables before finalizing.
Discover Methodologies
Check how other researchers framed their problem, methodology, and instruments for a similar type of study.
Save Time
Instead of manually scanning long lists of titles, let the system prioritize theses that are most relevant to you.
Tips for Getting Better Matches
- Use a complete abstract with background, objective, method, and key findings.
- Avoid abbreviations that are uncommon or not explained on first use.
- Be specific with variables, target users, and setting of the study.
- Run the tool again after refining your abstract to see updated suggestions.