Semantic Scholar - Article Summaries
Semantic Scholar uses GenAI tools to create summaries of journal articles, but don’t depend on the summaries being 100% accurate! Semantic Scholar summaries are typically better than general GenAI tool summaries but can still “make stuff up” so you still need to make your own summaries for articles you’ll directly use in your research.
Semantic Scholar Training Data sources: Web scraping (including Open Access data) & Indexing agreements with 50 publishers.
- Let’s do a keyword search in Semantic Scholar on a topic of interest.
- Open Semantic Scholar (no need to create an account unless you want to).
- Type the following into the search bar and click the search button (note that as of June 2024 Semantic Scholar does not support natural language queries, so we will do a keyword search. Semantic Scholar does use Generative AI for other aspects of its service):
Informal credentialling academic makerspaces - Try searching for information about a topic that you are interested in to further explore the capabilities of Perplexity. Be curious and have some fun!
- Test Semantic Scholar on a topic you know a lot about:
- In Semantic Scholar, try doing a keyword search for a topic you know a lot about so that you can evaluate the quality of the results.
- Now try the same search in Google Scholar and compare the Semantic Scholar results with Google Scholar.
- Reflection time:
- How useful do the articles make informal credentialing look for academic makerspace skills in student job searches?
- Compared to the Google Scholar results, how high is the quality of the articles Semantic Scholar found?
- Do the article summaries look reasonable?
- How can you verify the accuracy of the summary?
- Does this look like a tool that could help you with your research?