The Scenario
You manage PR for a mid-size B2B software company. Every Monday morning you pull together a media coverage report for the client — articles mentioning them, their competitors, or key industry topics from the past week.
Your current process: you have a Google Alerts email digest that you scan manually, clicking through to each article and copying headline, publication, author, and date into an Excel workbook. Last Monday you had 75 URLs to process. It took two and a half hours, most of which was clicking, waiting for pages to load, and hunting for bylines that some publications bury at the bottom of the article.
The client's CMO asked last week if you could send the report by 9 AM instead of noon.
The bad version:
- Open each article URL, wait for the page to load, look for the headline — which is sometimes in an h1 tag, sometimes in a meta title, and sometimes neither matches what you actually want
- Search the page for the author byline — which might be at the top, bottom, in a sidebar, or missing entirely — then switch to the workbook and type it in
- Try to find the publish date, realize it shows "3 days ago" not an actual date, open the article's source to find the ISO timestamp, convert it mentally to a readable format
Seventy-five articles times three minutes each is 225 minutes. That is why the report goes out at noon.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Excel workbook. It reads your URL list and through its built-in ScrapingAnt integration it scrapes each article, extracts the four structured fields you need, and writes them into the adjacent columns in one pass.
Open the SheetXAI sidebar and ask:
Scrape all 75 news article URLs in column A with ScrapingAnt and fill in Headline, Publication, Author, and Date columns in my Excel workbook
SheetXAI processes all 75 URLs through ScrapingAnt, which renders each page fully and extracts headline, publication, author, and publish date from the rendered content. Dates are written in a consistent MM/DD/YYYY format rather than relative timestamps. Missing authors are written as "Author not listed" rather than left blank.
What You Get
- Column B: article headline
- Column C: publication name
- Column D: author name, or "Author not listed" if not found
- Column E: publish date in MM/DD/YYYY format
- Rows where the URL returned a 404 or paywall flagged with "Unavailable" in column B
What If the Data Is Not Quite Ready
Some articles are on paywalled sites — you only get a preview paragraph
For each URL in column A, use ScrapingAnt to scrape the article; if the full body is not accessible, extract whatever headline and metadata are visible and write "Paywall — partial data" in column F
The URL list has articles in multiple languages — you need to know which ones are non-English
After extracting headline, publication, author, and date into columns B through E, add a column F where you detect the article language from the headline or visible text and write the language name
Some publications list multiple authors — you want all of them, not just the first
For each URL in column A, use ScrapingAnt to extract all byline names and write them as a comma-separated list into column D
Full media monitoring pass in one ask: scrape, extract, classify, and score relevance
Scrape all 75 news URLs in column A with ScrapingAnt; write headline into column B, publication into column C, author into column D, publish date into column E; in column F classify the article as "Direct mention," "Competitor mention," or "Industry topic" based on the article text; in column G rate relevance to the client on a scale of 1 to 3
Classification and scoring happen inline, so the report ships with context rather than just a list of links.
Try It
If your Google Alerts digest turns into a Monday morning data entry session — Get the 7-day free trial of SheetXAI and open your article URL list in an Excel workbook. Ask it to extract headline, publication, author, and date from every row using ScrapingAnt. Related: how to extract article content as Markdown, and the hub overview for all ScrapingAnt use cases.