Extract Text Snippets for Research Questions From Semantic Scholar Into a Google Sheet

The Scenario

A systematic-review team is six weeks into the evidence-coding phase. They have 30 research questions in column A and need the 5 most relevant text snippets per question — with paper title and year — pasted into adjacent columns so the coder can assess each excerpt without opening a separate browser tab for every paper. The team lead asked for this to be ready before the Wednesday morning coding sprint. It's Tuesday afternoon.

The bad version:

Run research question 1 through Semantic Scholar's text-search interface, read the snippet previews in the results, copy the 5 best excerpts, switch to the sheet, paste into columns B through F — then manually note the paper title and year for each.
Repeat for question 2. Realize the snippet format from the web interface wraps across lines differently each time, so the pasted text keeps breaking the row height.
By question 9 it's 6 PM and you have 21 to go.

The coding sprint is supposed to be the intellectually demanding part of this project. Setting up the input data for it shouldn't be what consumes the evening before.

The Easy Way: One Prompt in SheetXAI

SheetXAI is an AI agent that lives inside your Google Sheet. It reads the research questions in column A, runs a Semantic Scholar text-snippet search for each one, and writes the top 5 excerpts with paper title and year into adjacent columns.

Here is the prompt for this task:

For each research question in column A, run a Semantic Scholar text-snippet search and return the top 5 matching excerpts with paper title, authors, and year into columns B through F

What You Get

Columns B through F filled for each research question: Excerpt 1 through Excerpt 5, each formatted as "Excerpt text [Paper Title, Year]".
Each snippet is a verbatim passage from the source paper — citation-ready for evidence coding.
Questions that returned fewer than 5 high-relevance snippets show as many as were found, with blank cells for the remainder rather than fabricated text.
Paper titles and years are consistent across columns — no reformatting needed before the coding sprint.

What If the Data Is Not Quite Ready

Research questions in column A are phrased inconsistently — some too long for a clean snippet search

Before searching, shorten each research question in column A to its core searchable phrase (under 12 words), then run Semantic Scholar text-snippet searches and write the top 5 excerpts with paper title and year into columns B through F

You want to restrict snippets to papers published after a specific year

For each research question in column A, run a Semantic Scholar text-snippet search filtered to papers published after 2018, return the top 5 excerpts with paper title and year, and write them into columns B through F

You need to track which source papers appear across multiple questions

After filling snippet results into columns B through F for each question in column A, compile a summary on a new sheet called SourceFrequency showing each paper title that appeared as a source and how many different questions it contributed snippets to

Clean questions, search, restrict by date, and flag repeated sources in one pass

Trim and normalize each research question in column A, search Semantic Scholar for the top 3 text snippets per question from papers published after 2019, write each excerpt with paper title and year into columns B through D, and flag any paper title that appears for more than 4 different questions as High-Relevance in a separate sheet

Try It

Get the 7-day free trial of SheetXAI and open any sheet with a column of research questions from your evidence-coding protocol. Ask SheetXAI to pull the top Semantic Scholar text snippets per question — and start Wednesday's coding sprint with the inputs already in place.