Hey folks! I was deep in a research rabbit hole this week, trying to automate some literature reviews, and I stumbled onto a Scholarcy feature I hadn't fully appreciated before: **custom keyword biasing**.
We all know Scholarcy pulls out key concepts, but if you're working in a niche area (like, say, a specific AWS service or a security framework), the default extraction can sometimes miss your most critical terms. It turns out you can give it a nudge!
Here’s the gist: You can provide Scholarcy with a list of your own keywords and phrases. The tool then prioritizes sentences containing these terms during its summarization and highlighting process. This is a game-changer for technical papers.
**Example from my world:** I was reviewing a paper on cloud cost anomaly detection. My custom keyword list looked like this:
```
- Cost Explorer
- AWS Budgets
- anomaly detection algorithm
- CUR (Cost and Usage Report)
- tagging strategy
- idle resources
```
By feeding this in, the summary cards heavily featured sections discussing AWS-native tools and specific algorithms, which was exactly what I needed for my Terraform/Ansible automation project.
It feels a bit like training a small model on your own domain! 🚀
**How I'm using it now:**
* Pre-processing batches of PDFs for a project and using a different keyword file for each sub-topic.
* It makes creating structured knowledge bases from papers much faster. I can almost imagine piping summaries into a CI/CD pipeline to auto-update internal docs... (future project idea!).
Has anyone else played with this feature? I’d love to hear how you’re integrating it into your research or automation workflows. Any cool scripts to share for batch processing?
~CloudOps
Infrastructure as code is the only way
Yeah, that's a solid use case. I use a similar trick with automated commit message tagging in CI.
For example, you can bias a parsing step to flag commits mentioning specific Jira ticket prefixes or security tags. Lets you auto-route code reviews or trigger specific pipeline stages.
The caveat is you need a clean, focused list. If you dump 50 keywords in, the signal gets drowned. Start with your top 5-10 critical terms.
Ship fast, review slower
That's a clever find. I've seen similar bias features in a few other extraction tools (like Semantic Scholar's API or even some custom GPT wrappers), but Scholarcy's implementation is particularly clean for this use case.
One thing I'd add: be careful about overlapping keywords. In your example, "Cost Explorer" and "AWS Budgets" might both appear in a sentence about "cost anomaly." If Scholarcy doesn't handle deduplication well, you could end up with redundant summary snippets that emphasize the same paragraph from slightly different angles. I've found it helps to run a quick test with a known paper and tweak the list iteratively rather than dumping everything in one shot.
Also curious - have you tried combining this with Scholarcy's filtering by section types (like abstract vs. methodology)? That could narrow down even further if you're only interested in algorithm descriptions.
Keep it real