Structured Interaction Summarization

• Structured interaction summarization is a method that segments and condenses complex interactions into semantically organized outputs using explicit cues like section headers and latent dependencies.
• Techniques include pairing document sections, building dependency and graph-based models, and leveraging neural architectures to enhance factual consistency and interpretability.
• Current approaches integrate extractive and abstractive methods, use incremental updates with structured memory, and apply multi-modal graphs to improve summary quality and practical usability.

Structured interaction summarization encompasses a family of methods that seek to capture, organize, and condense complex, multi-faceted interactions—ranging from scientific publications and multi-party dialogues to heterogeneous network interactions—into semantically structured, interpretable outputs. In contrast to flat, unstructured abstraction, these techniques exploit explicit or latent structural cues, enable decomposition across interaction units or modalities, and typically result in summaries or intermediate representations aligned with the inherent organization of the underlying data.

1. Foundational Principles and Definitions

At its core, structured interaction summarization addresses the need for reducing high-dimensional or long-form interactive data, such as academic papers, conversations, and transactional logs, into structured summaries that retain critical information, relationships, and interpretability. This field extends standard summarization by:

• Segmenting interactions into meaningful units (e.g., sections, turns, events) and mapping these to structured outputs (e.g., labeled summaries, triples, graphs);
• Leveraging structural signals—either explicit (section headers, interaction roles) or latent (learned sentence dependencies, coreference graphs)—to inform content selection, abstraction, and condensation;
• Supporting both extractive and abstractive paradigms, with a bias toward multi-level or multi-granular representation.

Papers such as “Structured Summarization of Academic Publications” (Gidiotis et al., 2019) and “StructSum: Summarization via Structured Representations” (Balachandran et al., 2020) provide early and influential formalisms, elucidating how explicit and learned structure can be interwoven with state-of-the-art summarization architectures.

2. Methodologies for Structured Summarization

Structured interaction summarization methodologies can be organized according to (a) input structure modeling, (b) training paradigms, (c) integration with neural models, and (d) output structure enforcement.

a. Structure-Induced Data Pairing

• SUSIE (Gidiotis et al., 2019): Annotates document sections via keyword heuristics from XML headings, mapping “methods”, “results,” etc., to canonical labels, and pairs each section of the full text with its abstract counterpart; training examples are thus aligned at the section level.
• PMC-SA (Gidiotis et al., 2019): Serves as a large-scale corpus for learning from such pairings, using IMRD (Introduction, Methods, Results, Discussion) alignments.

b. Graph-Based Representation and Reasoning

• StructSum (Balachandran et al., 2020): Computes both latent dependency trees (non-projective) among sentences—using a bilinear scorer and Kirchhoff’s matrix-tree theorem for normalization—and explicit, coreference-derived sentence graphs; these are encoded via attention and concatenated with standard sentence embeddings.
• GraphHINGE (Jin et al., 2020): Models interactions between node neighborhoods in a heterogeneous information network using FFT-accelerated convolution over paths defined by semantic metapaths; element- and path-level attention aggregates the resulting interaction values.
• CSS-GR (Kim et al., 26 Mar 2025): Constructs dynamic graphs across text and vision modalities, connects nodes by cosine similarity thresholds, augments node features via message passing, and maintains a global state for high-level reasoning using state-space models.

c. Structured Decoding and Output Construction

• Section-wise and stepwise models: SUSIE (Gidiotis et al., 2019) generates summaries for each section individually. Stepwise structured transformers (Narayan et al., 2020) generate content units (e.g., sentences, table records) iteratively, conditioning each choice on the evolving summary.
• Multi-granularity decoding: S-BART (Chen et al., 2021) fuses token-level, discourse graph, and action graph representations via custom cross-attention modules and ReZero-style residuals.
• Unified generation: Structured summarization models (Inan et al., 2022) simultaneously produce segmentation boundaries and segment-level natural language labels in a single encoder–decoder pass, casting the joint task as a sequence generation problem.

d. Structured Memory and Incremental Update

• Chain-of-Key strategies (Hwang et al., 21 Jul 2024): Organize summary information in JSON schemas, enabling efficient partial updates by augmenting or appending to structured keys rather than re-generating summaries for new data.

3. Model Architectures and Structural Integration

The leading approaches demonstrate a spectrum of integration strategies, often hybridizing structure-aware components with mainstream neural architectures.

Method	Structural Layer	Integration Point
SUSIE (Gidiotis et al., 2019)	Section annotation	Training pair and truncation
StructSum (Balachandran et al., 2020)	Latent/explicit graphs	Encoder-level attention
GraphHINGE (Jin et al., 2020)	Pathwise graphs (HIN)	FFT-conv, attention in graph
S-BART (Chen et al., 2021)	Discourse/Action graphs	Decoder cross-attn + gating
Incremental CoK (Hwang et al., 21 Jul 2024)	JSON-structured schema	Update via key-paths, not text
NexusSum (Kim et al., 30 May 2025)	Dialogue transformation	Preprocessing + chunk hierarchy
StrucSum (Yuan et al., 29 May 2025)	Sentence-level graphs	LLM prompting via TAG context

Model architectures benefit from:

• Hierarchical processing (e.g., HiBERT, NexusSum);
• Multi-agent or multi-stage pipelines (NexusSum, ScoreRAG (Lin et al., 4 Jun 2025));
• Joint learning of segmentation and labeling for interactions lacking rigid structure (Inan et al., 2022);
• Discrete role/filler separation in tensor-product transformers (Jiang et al., 2021).

4. Evaluation Metrics and Empirical Outcomes

The adoption of structural interaction summarization methods has resulted in demonstrable gains:

• SUSIE (Gidiotis et al., 2019): ROUGE improvements of up to 4 points, with pointer-generator coverage models seeing 13% (ROUGE-1), 28% (ROUGE-2), and 14% (ROUGE-L) relative boosts over flat baselines.
• StructSum (Balachandran et al., 2020): Roughly 1.08 ROUGE-L gain, increased coverage (copy proportion from 12.1% to 24.0%), and a 14.7% rise in novel n-gram generation.
• Stepwise structured transformers (Narayan et al., 2020): ROUGE-1 ≈ 43.8 and ROUGE-2 ≈ 20.8, state-of-the-art for CNN/DailyMail extractive summarization.
• Incremental structured memory (Hwang et al., 21 Jul 2024): Up to 40% and 14% F₁ increases (SUMIE, BooookScore), with further 7%/4% enhancements via chain-of-key JSON updating.
• ScoreRAG (Lin et al., 4 Jun 2025): LLM evaluation scores of 4.64 (vs 4.34 baseline), expert-rated improvement in accuracy and informativeness.
• NexusSum (Kim et al., 30 May 2025): Up to 30.0% improvement in BERTScore (F1) for long-form narrative summarization.

Domain- or task-specific metrics (e.g., FactCC, SummaC, GPTScore, task-oriented dialogue F1) are employed where fine-grained factual consistency and explainability are crucial.

5. Domain Applications and Extensions

Structured interaction summarization methods show broad utility, including:

• Scientific literature and technical document summarization, especially where IMRD or other canonical sectioning is present (Gidiotis et al., 2019, Jaradeh et al., 2022);
• Meeting, dialogue, and conversational summarization using discourse and action graph structures (Chen et al., 2021, Zhao et al., 2021);
• Online collaborative and deliberative environments where real-time “living summaries” interleave with discussion (Wikum+ (Tian et al., 2020));
• Multi-modal cross-domain summarization (text and images/video) with cross-modal graphs and state-space reasoning (Kim et al., 26 Mar 2025);
• Incremental session logs or provenance summaries for intelligence analysis and collaborative workflow communication (Block et al., 6 Sep 2024);
• Explainable recommendation and user/item profiling via hierarchical interaction-derived textual representations (Liu et al., 8 Jul 2025);
• Zero-shot and low-resource scenarios where structure-augmented prompting obviates the need for training (Yuan et al., 29 May 2025).

6. Interpretability, Transparency, and Future Directions

Interpretability is a central advantage of structured interaction summarization. Methods such as latent dependency induction (Balachandran et al., 2020), action triple extraction (Chen et al., 2021), explicit aspect-triple rationales (Jiang et al., 15 Mar 2024), and TAG-driven prompting (Yuan et al., 29 May 2025) provide intermediate signals that are readily mapped back to the interaction data. This transparency supports downstream applications including moderation, user coaching, news dispatching, recommendation explanation, knowledge graph construction, and scientific indexing.

Open challenges and future directions highlighted include:

• More advanced modeling of cross-turn and cross-participant relational dependencies in dialogue;
• Generalization to multi-modal and multi-source scenarios where both intra- and cross-modality structure is essential (Kim et al., 26 Mar 2025);
• Adaptive, user-controllable segmentation for provenance and analytic workflows (Block et al., 6 Sep 2024);
• Robust, redundancy-minimizing update mechanisms for long-horizon incremental summarization (Hwang et al., 21 Jul 2024);
• Enhanced fact verification and hallucination avoidance through structured retrieval and grounding (as in ScoreRAG (Lin et al., 4 Jun 2025));
• Deeper integration of explainability and user trust protocols for recommendation and user-facing summaries (Liu et al., 8 Jul 2025).

The field continues to progress toward producing summaries that not only condense interaction data but also expose underlying relational structure and rationales, enabling both humans and intelligent systems to interpret, verify, and build upon them in complex information environments.