Fact extraction is the task of turning a passage into statements that can be examined individually. It is a preparation step, not a verification result. An extraction API can identify what a document asserts while remaining completely agnostic about whether those assertions are supported. That distinction matters when the input comes from a generated answer, an advertisement, a journal article, or a copied social post.
The design goal is not to produce the largest possible list of fragments. It is to preserve the meaning of the original text while creating useful units for retrieval and review. This guide presents an implementation checklist for an illustrative extraction workflow, with particular attention to qualifiers, source spans, and the mistakes that can change a claim before anyone checks it.
Separate extraction from verification
The original FEVER research paper describes a fact-verification dataset whose claims are labeled Supported, Refuted, or NotEnoughInfo, with evidence recorded for the supported and refuted cases. Its separation between a claim and the evidence needed to assess it is a useful conceptual reference. Your extraction component should not treat the act of finding a statement as evidence that the statement is correct.
A clean response can therefore say “claim extracted” while leaving the assessment unassigned. Reserve verification fields for a later stage. This avoids an easy integration mistake: a downstream interface sees an object named fact and assumes it has already been checked. Naming the object claim is a simple way to make that boundary more visible.
Decide what counts as a checkable statement
Begin with a written annotation policy. Dates, quantities, reported events, attributed quotations, and descriptions of specific relationships are common candidates. Opinions and preferences may not be independently verifiable, although the fact that someone expressed an opinion can be. “This is the best archive” and “The director called this the best archive” require different treatment.
Define how your system handles recommendations, predictions, and conditional statements. A prediction can be extracted as a prediction without being labeled a present-day fact. A condition should remain attached to its consequence. “The service is free for students” must not become “The service is free.” These examples are not edge cases to hide in a footnote; they belong in the initial extraction specification and the test collection.
Split compound claims without destroying context
Imagine the fictional sentence, “In 2022, the Meridian catalog added four regional collections and introduced a public export.” A sensible extraction might produce one claim about the additions and another about the export, each preserving the year and the named catalog. Removing shared context to make each fragment shorter can make the resulting statements ambiguous.
Use an atomicity rule that favors independent assessment rather than grammatical minimalism. A statement may contain several words or clauses that must stay together to retain its meaning. Record parent-child relationships when a sentence produces multiple claims. That lets a reviewer reconstruct the original passage and lets the publishing interface show that only one part of a sentence remains unsupported. Avoid joining separate clauses simply because they mention the same entity.
Preserve attribution, negation, and uncertainty
Attribution tells you whose assertion you are extracting. “The report estimates ten thousand visits” is not equivalent to “There were ten thousand visits.” Preserve words such as estimates, alleges, projects, approximately, and according to. They change the strength or source of the assertion and may determine what evidence would actually support it.
Negation deserves explicit tests. “No change was observed” must not become “A change was observed,” and “the study did not establish an effect” is not automatically a claim that no effect exists. A useful review view places the original passage beside the normalized wording so that these shifts are visible. Flag uncertain normalizations for review rather than asking a verifier to repair a distorted claim later in the pipeline.
Give every claim a reliable location
Store the original document identifier and a reproducible locator. For plain text, character offsets can work when the text normalization process is fixed. For structured documents, use a section identifier and paragraph reference. If the input is transformed before extraction, preserve the version or hash of the text to which those locations refer.
A locator is useful only when another component can resolve it. Test what happens after whitespace normalization, document conversion, or a new parser version. Do not silently attach offsets from one version to another. Include a short original span in the extraction response when permitted, because it helps reviewers spot alignment errors immediately. The normalized claim and the original span should be separate fields, not alternative values of the same field.
Normalize entities conservatively
A short name may refer to several people, organizations, publications, or projects. Resolve it only when the surrounding text provides enough context or an approved entity reference confirms the match. A model’s familiarity with a popular entity is not a good reason to replace an ambiguous name in the source.
Keep an unresolved identifier when necessary. Record candidate matches separately from a confirmed resolution, and preserve aliases rather than overwriting the original form. Apply the same care to dates, currencies, units, and geographic references. A date written as 03/04 can be ambiguous, and a quantity without a unit may be unusable for comparison. The appropriate response is often to carry the ambiguity forward, not to invent the missing precision.
Control duplicate claims and document scale
Long documents repeat assertions in summaries, captions, introductions, and conclusions. Deduplication can reduce review work, but it should preserve every important source location. Two similar sentences may differ in period, population, or scope. Treat semantic similarity as a candidate for grouping, not automatic proof of equivalence.
For large inputs, process sections with enough surrounding context to retain references and qualifiers. Keep a document-level index so claims from separate sections can be reconciled without losing their origins. Set an explicit truncation status when a processing limit is reached. Returning only the first portion of a document without disclosing that limit can create a misleading impression that the entire document has been inspected. Coverage is part of the extraction result.
Evaluate extraction with a reviewable rubric
Measure whether the output includes the important checkable assertions, preserves their meaning, and links them to the correct text. Review omissions, invented claims, excessive splitting, and incorrect merging separately. A single aggregate score can hide a system that extracts many easy claims while missing the one consequential assertion in the conclusion.
Create a small set of adjudicated examples with original spans and acceptable claim formulations. Include quotations, nested attribution, tables described in prose, uncertain quantities, and references that cross paragraph boundaries. Ask reviewers to explain disagreements about atomicity rather than treating one wording as uniquely correct. An extraction can be semantically faithful in more than one form; the important requirement is that the downstream verification task remains the same.
Define the handoff to the evidence stage
The verification component should receive the original wording, normalized wording, context, location, and any unresolved ambiguity. Include processing metadata that identifies the extraction configuration. Keep extraction confidence, when used, separate from factual support: confidence that a sentence was parsed correctly is not confidence that its content is true.
Document the failure cases that should stop automatic assessment. An unreadable table, missing antecedent, or uncertain entity match may require a reviewer before retrieval begins. Review the fact-extraction topic guide for the field checklist and the structured-data article for a provenance-oriented response pattern. A strong extraction API leaves the next stage with a faithful question to answer, not a polished statement whose meaning has quietly changed.
