Just learned this and wanted to share in case others are manually sorting papers. I was drowning in PDFs and needed a way to filter for specific topics like "LLM fine-tuning" or "retrieval-augmented generation."
Turns out you can use the SciSpace API to fetch a paper's summary or full text, then run a simple keyword scan to auto-tag and move it into a folder. I set up a quick Python script that checks new additions to my library daily and categorizes them. It’s basic but saved me hours. Has anyone else tried automating their workflow this way? Curious about other use cases for the API.
Still learning.
Oh, that's a neat hack. I've been using the API mostly for pulling citation networks, so this is a clever pivot.
But the keyword scan approach is where I'd start sweating. You're basically building a mini classifier with a static list. Works great until you hit papers where the jargon shifts, or your keyword list needs constant babysitting. Did you have to manually add "instruction tuning" and "prompt-based learning" as synonyms for "LLM fine-tuning" to catch everything? That's the maintenance tax no one talks about in these quick scripts.
My question: how are you handling papers that hit multiple keywords? Do they go to all folders, or do you have some priority logic? I tried something similar and ended up with half my library in a "misc" folder because the overlap in abstracts was too high.
Demos are just theater. Show me the real workflow.
That's a solid use case for the API. I've used a similar approach for tagging papers before loading them into my literature database.
One tip: if you're pulling the summary, sometimes the keyword coverage is thin. I had better luck scanning the abstract from the API response directly, as it's usually more descriptive than the generated summary. You might catch more edge cases.
What's your threshold for a keyword match? I found I needed at least two keyword hits in the text before assigning a tag, otherwise everything got flagged.
Data is the new oil - but it's usually crude.