Summarize Long Articles Quickly
Get a concise summary of lengthy web articles or documents in seconds.
Analyst
What it does
Get a concise summary of lengthy web articles or documents in seconds.
What it does
Copy the text of a long-form article, report, or blog post and paste it into the chat. Request a brief overview that highlights the main arguments, key data points, and any conclusions, saving time on reading dense material.
Example output
Summary of Remote Work Impact Analysis
- Main Argument: The impact of remote work on productivity is sector-dependent, with significant divergence between technical and creative industries.
- Key Data: A study by Dr. Aris Thorne reveals a 15% productivity increase in tech sectors but an 8% decrease in creative fields due to communication barriers. Additionally, Fortune 500 companies saw a 20% rise in hybrid model adoption in Q3 2023.
- Recommendation: To address isolation and maintain team cohesion, the authors recommend implementing structured virtual collaboration hours.
Example prompt
Please summarize the following article: [paste full text of article here] Provide a concise overview that includes:
- The main arguments or thesis statement.
- Key data points and supporting evidence.
- Any final conclusions or recommendations. Keep the summary brief, focusing only on the most critical information to save reading time.
How to build it
Open your chat agent (ChatGPT, Claude, or Copilot) and paste the example prompt. Adjust the inputs in the curly braces and run.
The agent brief
Everything your agent needs, including the gotchas. Copy it and go.
You are helping me build the following AI agent workflow. ## Goal Summarize Long Articles Quickly: Get a concise summary of lengthy web articles or documents in seconds. ## Specification - What it does: Get a concise summary of lengthy web articles or documents in seconds. - Trigger: Run manually (Manual · on demand) - Autonomy: You stay in control - Expected setup effort: under an hour - Tools/services involved: ## Known pitfalls, handle each one explicitly in your implementation No documented pitfalls for this recipe. Apply your own review before going live. ## Reference implementation https://learn.chatgpt.com/use-cases (vendor case study) Fetch and inspect this before building. If it matches my stack, adapt it; if not, rebuild the pattern with my tools. ## Process requirements 1. Before building: ask me which of the listed tools I actually use and what my platform is (n8n / Make / code / other). Do not assume. 2. Adapt the pattern to my answers; do not force the reference stack. 3. Address every pitfall above; tell me how you handled each. 4. Provide a test plan I can run before letting this touch real data. 5. Ask before any step that sends messages, modifies data, or spends money. Source: https://usecasesforagents.com/use-case/summarize-long-articles-quickly-e4f5/ via usecasesforagents.com
Want this running in your business?
This is what I do. I design and build AI agents like this one, and keep them running. If you want it set up for your team instead of doing it yourself, get in touch.
Who it's for
Also fits Analyst.