Atomic Sentence Extraction Overview

• Atomic sentence extraction is the process of breaking down complex, multi-propositional sentences into minimal, self-contained units that each convey a single idea.
• It employs both rule-based methods using dependency parses and LLM-driven approaches with iterative reasoning to ensure grammaticality and semantic precision.
• This technique improves applications in fact verification, knowledge graph induction, and automated reasoning by providing clear, independently verifiable assertions.

Atomic sentence extraction is the process of decomposing complex, often multi-propositional, sentences into minimal, self-contained units—each conveying a single, unambiguous idea. These "atomic sentences" are crucial for information retrieval, fact verification, question answering, automated reasoning, and knowledge graph construction, as they enable finer semantic granularity and more reliable downstream reasoning and matching (Kamana et al., 1 Jan 2026, Lairgi et al., 26 Oct 2025, Zheng et al., 9 Jun 2025, Stacey et al., 2023, Srikanth et al., 12 Feb 2025). Atomic extraction protocols may be rule-based, leveraging explicit linguistic structures, or LLM-driven, relying on semantically-informed decomposition patterns.

1. Formal Definitions and Linguistic Criteria

An atomic sentence, also termed an atomic fact or atom, is a minimal, self-contained snippet that expresses exactly one proposition, typically in the form of a subject–predicate–object assertion (Lairgi et al., 26 Oct 2025, Srikanth et al., 12 Feb 2025, Stacey et al., 2023, Kamana et al., 1 Jan 2026). Each atomic sentence must be:

• Grammatical and well-formed.
• Fully entailing a distinct fact from the source text.
• Semantically atomic: expressing a single event, relationship, or property, with all entities and temporal references explicitly instantiated.
• As concise as possible, to minimize ambiguity and facilitate robust LLM processing.

Atomic decomposition notably aligns with Neo-Davidsonian event semantics, where each participant or event instance is isolated as an independent proposition (Srikanth et al., 12 Feb 2025). In temporal knowledge extraction, an atomic fact is often linked to both observation and validity time intervals (Lairgi et al., 26 Oct 2025).

2. Rule-Based and Dependency-Driven Extraction Methods

Rule-based atomic extraction systems use explicit syntactic cues to identify split points. A prominent framework applies iterative rule sets over dependency parses to target the following clause types (Kamana et al., 1 Jan 2026):

• Relative clauses ("relcl"): Extracts propositions introduced by relative pronouns or subordination.
• Appositions ("appos"): Splits appositive phrases as standalone assertions about the head noun.
• Coordinated predicates/objects ("conj"): Separates coordinated verbs or noun phrases into distinct events or entity assertions.
• Adverbial and subordinate clauses ("advcl", "ccomp"): Isolates temporal, causal, or conditional adjuncts.
• Passive constructions: Recovers hidden logical subjects through argument propagation.

The extraction algorithm processes each rule in a fine-to-coarse sequence: detach relative and appositive clauses, split coordinations, extract adverbials, and finally, handle passive subject/object restoration. Each resulting atomic sentence is constructed to have its own explicit subject and predicate—if a clause lacks a subject, the nearest noun phrase from the main clause is copied.

Example decomposition:

• Input: "Anna ate an apple and a banana."
• Output: "Anna ate an apple." / "Anna ate a banana."

Detailed algorithmic pseudocode and rule manipulation are implemented in mainstream dependency parsing toolkits, such as spaCy (Kamana et al., 1 Jan 2026).

3. LLM-Driven and Iterative Extraction Approaches

LLMs offer an alternative, semantically-informed approach, decomposing complex sentences into atoms via in-context prompting and iterative reasoning (Stacey et al., 2023, Lairgi et al., 26 Oct 2025, Srikanth et al., 12 Feb 2025, Zheng et al., 9 Jun 2025).

The general pipeline involves:

• Prompt construction: Using exemplars, prompt the LLM to "List all minimal facts entailed by [sentence]."
• Draft generation: Two or more LLM responses are merged to produce an initial set of candidate atoms.
• Pruning and refinement: Atoms are filtered using an NLI model to ensure entailment and grammaticality; duplicates or unsupported claims are removed (Srikanth et al., 12 Feb 2025, Stacey et al., 2023).
• Iterative coverage checking: The LLM, often with the aid of internally verified partial atoms, determines when all information has been decomposed. This coverage criterion is LLM-judged or specified in the prompt (Zheng et al., 9 Jun 2025).
• Temporal attachment: In dynamic knowledge graph extraction, each atomic fact is associated with an observation time and, if applicable, a validity interval (Lairgi et al., 26 Oct 2025).

The process is adaptive: LLMs update their decomposition based on the verification results of previously extracted atoms, evidence retrieval, and specific demonstration examples relevant to the task or original text (Zheng et al., 9 Jun 2025).

4. Algorithms and Parallelized Extraction Pipelines

For both dependency-based and LLM-centric extraction setups, scalability is addressed through parallelization and chunking:

• Chunk-level LLM extraction: Input documents are split into ≤400-token chunks, each of which is atomized independently and in parallel for latency control and exhaustivity (Lairgi et al., 26 Oct 2025).
• Parallel knowledge graph merges: Each atomic fact is post-processed to yield entity–relation–object–time tuples. Merging of partial subgraphs is embedding-driven and batched in $O(\log n)$ depth to allow tractable large-scale graph construction.
• Rule-based iteration: Rule passes are sequenced, with each pass applying to all partial sentences remaining after the previous pass. Subject and object restoration, linearization, and final formatting are performed in a last sweep (Kamana et al., 1 Jan 2026).

Pseudocode representing these procedures—such as LLM-then-NLI-then-human filtering, iterative atom extraction conditioned on prior results, and entity/relation/temporal resolution—are consistently adopted in both verification and knowledge graph contexts (Lairgi et al., 26 Oct 2025, Kamana et al., 1 Jan 2026, Srikanth et al., 12 Feb 2025, Zheng et al., 9 Jun 2025).

5. Evaluation Metrics and Empirical Results

Evaluation of atomic sentence extraction is based on both intrinsic and extrinsic metrics:

• ROUGE (ROUGE-1, ROUGE-2, ROUGE-L): Token and subsequence overlap with gold-standard splits.
• BERTScore: Semantic similarity between system and reference atoms using contextual embeddings (e.g., RoBERTa-large).
• Recall/Exhaustivity: Proportion of atomic facts in ground truth that are recovered by the system (Lairgi et al., 26 Oct 2025).
• Stability: Cosine similarity of the set of atomic fact embeddings across repeated extraction runs (Lairgi et al., 26 Oct 2025).
• Latency: Total wall-clock time for extraction and downstream merging (Lairgi et al., 26 Oct 2025).
• Logical Consistency/Atomic Accuracy: Agreement between atomic sub-problem judgements and the whole-problem inference label in tasks such as NLI, where atomic consistency is quantified separately from overall accuracy (Srikanth et al., 12 Feb 2025).

Empirical findings include:

• Rule-based extraction achieves ROUGE-1 F1 ≈ 0.67, ROUGE-2 F1 ≈ 0.48, ROUGE-L F1 ≈ 0.65, and BERTScore F1 ≈ 0.56 on WikiSplit (Kamana et al., 1 Jan 2026).
• LLM-driven atomic extraction (ATOM) yields ~31% factual and 18% temporal recall gains in TKG construction, improves stability by ~17 percentage points, and reduces merge latency by ~94% over baselines (Lairgi et al., 26 Oct 2025).
• In NLI settings, atomic decomposition exposes logical inconsistency: top LLMs reach only 88% atomic-to-overall consistency and 65–87% atomic accuracy, compared to 80–93% for instance-level scores (Srikanth et al., 12 Feb 2025).

6. Applications and Impact in Downstream Tasks

Atomic sentence extraction consistently improves performance, interpretability, and robustness in several application domains:

• Fact verification and misinformation detection: Atomic splitting enables fine-grained evidence retrieval and multi-hop reasoning over claims, reducing error propagation and boosting overall accuracy and F1 across PolitiHop, LIAR, HOVER, and similar datasets (Zheng et al., 9 Jun 2025).
• Knowledge graph induction: Decomposition into atomic facts ensures granular, temporal knowledge graphs with higher exhaustivity and robustness, supporting dynamic analytics and memory frameworks (Lairgi et al., 26 Oct 2025).
• Natural language inference (NLI): Models trained or evaluated on atomic sub-problems reveal the limitations of current architectures in performing consistent, logical reasoning; atom-based inference also provides interpretable justifications, enabling faithfulness audits (Stacey et al., 2023, Srikanth et al., 12 Feb 2025).
• Automated reasoning/question answering: Isolating premises into atomic sentences improves explicit symbolic inference and reduces ambiguity when matching facts to queries (Kamana et al., 1 Jan 2026).

A plausible implication is that future hybrid systems may combine rule-based pattern transparency (useful for targeted repair of failure cases) with the adaptivity and semantic flexibility of LLM-driven decomposition for optimal coverage and robustness.

7. Failure Modes, Challenges, and Open Directions

Atomic sentence extraction is sensitive to a range of linguistic and systemic challenges:

• Missing objects/arguments: Rule-based methods most often fail by neglecting necessary syntactic dependents, particularly objects in verb phrases (44.4% error incidence) (Kamana et al., 1 Jan 2026).
• Complex coordinations and nested structures: Difficulties in propagating subjects or handling ambiguities in coordinated and subordinate clause boundaries result in miscellaneous errors (39.3%), and specific coordination errors (5.2%) (Kamana et al., 1 Jan 2026).
• Appositive, adverbial, and relative clause handling: These constructions, while less frequent, induce meaning reversals, incomplete splits, or information loss when splitting criteria are insufficiently fine-tuned (Kamana et al., 1 Jan 2026).
• Stability and repeatability: LLM-based approaches, if not carefully controlled (e.g., consistent chunking, exhaustive prompting), may yield unstable or incomplete atom sets across runs (Lairgi et al., 26 Oct 2025).
• Logical consistency in downstream models: Even the highest-performing LLMs do not consistently propagate atomic-level inferences to aggregated decisions, exposing weaknesses in multi-step reasoning (Srikanth et al., 12 Feb 2025).

These observations highlight the need for more robust subject/object recovery logic, hybridization with semantically aware LLMs, and explicit atomic-to-overall consistency checks in downstream reasoning. Future development will likely focus on adaptive, neural-symbolic systems that blend the interpretability of rule-based extraction with LLM-cued semantic disambiguation, tightly coupling extraction with verification to ensure both coverage and faithfulness.