Extractability depends on clarity of structure. Machines prefer citing paragraphs with clear conclusions, defined boundaries, and verifiable sources over long sentences requiring repeated inference. Clear structure also benefits readers.
Key practices include: lead with the conclusion using short sentences; list steps in numbered order matching actual execution; and state conditions and exceptions separately to avoid confusion with main conclusions. This structure lets readers quickly determine if the guidance applies to their situation.
When presenting data, specify the scope and timeframe—for example, the statistical period or sample source. Numbers without cited sources are often overlooked; conclusions with clear origins are more likely to be adopted. When citing others' viewpoints, include attribution to respect the original author and facilitate verification.
Verify extraction effectiveness through self-check: hand the article to a colleague and ask them to state the conclusion after reading just one paragraph. If they need the full text to answer, the paragraph structure needs adjustment. Incorporate this check into your pre-publish workflow—it saves more time than fixing issues afterward.