Abstract Logic Writer#

Overview#

Use symbolic discourse constraints and a lightweight ontology to draft or critique English academic abstracts. Treat abstract writing as a constrained mapping from propositions to an ordered sentence sequence, not as free-form style imitation.

Core workflow#

1. Build a proposition set P = {background, status, motivation, challenge, idea, technique, evidence} from the user's notes.
2. Choose the shortest valid role chain whose image still contains motivation, challenge, and idea. The default 4-5 sentence chain is M -> C -> I -> T -> E, with optional background or status prepended.
3. For each sentence, write a micro-structure general -> specification -> consequence/purpose. Do not place a narrow detail before its governing concept.
4. Load references/computable-rules.md as the primary specification. Load references/lexeme-typing.md and assets/lexeme_types.json when verb-noun fit is uncertain.
5. If the domain terminology is sparse or unstable, load references/ontology-bootstrap.md and optionally run: python scripts/ontology_bootstrap.py --domain "..." --terms "term a,term b" --outdir ./ontology_out
6. Before finalizing, run: python scripts/abstract_lint.py draft.txt for rule diagnostics, and run python scripts/abstract_score.py draft.txt or python scripts/abstract_score.py before.txt --compare after.txt when a formal score or pairwise comparison is needed.

Drafting discipline#

• Assign each sentence exactly one primary discourse role.
• Never output a sentence that only labels a condition without causal or purposive load. Reject patterns like X is a challenge. unless the sentence continues with cause, consequence, or operational relevance.
• When introducing a new concept x, attach motivation, purpose, or consequence within the same sentence or an adjacent sentence.
• When explaining a mechanism, state what it enables, stabilizes, reduces, or preserves.
• Prefer typed predicate selection over idiomatic guesswork. Example: traffic grows, demand increases, applications develop, systems evolve, accuracy improves, continuity is maintained.
• Avoid common AI-sounding markers. Do not use the em dash or Unlike unless the user explicitly asks to preserve source wording.
• Do not end with a generic recap sentence. The last sentence must carry evidence, operational implication, or measured outcome.

Output modes#

1. Draft from notes#

Return:

1. an optional symbolic plan when the source notes are underspecified,
2. the final abstract,
3. concise lint notes only when there are nontrivial tradeoffs.

2. Critique or rewrite an existing abstract#

Return:

1. a violation list keyed to the symbolic predicates in references/computable-rules.md,
2. a repaired abstract,
3. the smallest possible set of lexical substitutions when the main issue is verb-noun mismatch.

3. Produce negative examples#

Use references/negative-examples.md. Generate intentionally flawed rewrites that violate one or more named predicates such as summary_only, selection_mismatch, scope_inversion, or forbidden_marker. Label each negative example with the violated rules. Do not present it as recommended style.

Resource map#

• README.md: GitHub-facing quick start and repository guide.
• references/computable-rules.md: formal sentence and discourse constraints.
• references/lexeme-typing.md: upper ontology for noun classes and verb selection.
• references/ontology-bootstrap.md: domain ontology construction and download workflow.
• references/negative-examples.md: contrastive negative examples and rule tags.
• references/source-abstract-corpus.md: raw domain corpus supplied by the user.
• scripts/abstract_lint.py: heuristic checker for role order, banned markers, and selection mismatches.
• scripts/abstract_score.py: formulaic scorer and comparator for one or two abstract fragments.
• scripts/ontology_bootstrap.py: generate a seed ontology or download a public ontology file.
• assets/discourse_rules.json: machine-readable role order, forbidden patterns, and score weights.
• assets/lexeme_types.json: machine-readable lexeme typing rules.
• examples/: before-and-after fragments for quick scoring demos.
• evals/: sample scoring outputs for repository documentation.

Working defaults#

When the user does not provide all paper details, infer the missing low-risk connective tissue from the available propositions and state the assumptions briefly. Keep the prose compact, domain-accurate, and hierarchy-aware. Prioritize logical fit over rhetorical flourish.