SummPilot: Adaptive Document Summarization

• SummPilot is an adaptive document summarization system that combines LLM-driven generation with graph-based content extraction for efficient and accurate summaries.
• It features dual modes—a one-click automatic mode and an advanced interactive mode with semantic graph manipulation and explainable evaluation for refined outputs.
• Empirical evaluations demonstrate high performance with metrics like ROUGE-1 F1 up to 47.37 and improved real-time handling in customer support applications.

SummPilot is a class of document summarization systems characterized by their emphasis on efficiency, customization, and interactive user control, with architectural instantiations spanning LLM-augmented frameworks, unsupervised scientific pipelines, and real-time incremental note-taking for enterprise applications. In its canonical LLM-driven form, SummPilot tightly integrates LLMs with graph-based visualization and user-editable abstraction layers, promoting both rapid, fully automatic summarization and granular, hands-on refinement. Variants adapted for scientific literature and production customer-support environments demonstrate the extensibility of the SummPilot paradigm, which consistently blends prompt engineering, graph or cluster construction, rigorous evaluation metrics, and feedback-informed retraining for high-quality, user-tailored summaries.

1. Architectural Principles and System Modes

SummPilot architectures, as exemplified by the 2026 system leveraging GPT-4o, adopt a modular design in which user interaction depth is progressively adjustable. The pipeline operates in two principal modes (Yun et al., 13 Jan 2026):

• Basic Mode: A one-click summary generation process wherein one or more source documents are passed to an LLM (GPT-4o) using a template prompt. The system returns an abstractive summary that strives for coherence and compression.
• Advanced Mode: Invokes three interactive modules: semantic graph construction, entity clustering, and explainable evaluation. Users can refine summaries by manipulating graph nodes, toggling entity clusters, and receiving transparent feedback on length, coverage, and factual consistency of each summary draft.

This dual-mode approach is also reflected in other SummPilot instances. For example, the scientific summarization pipeline (SciSummPip-derived) maintains a fully unsupervised flow, but allows iterative re-tuning of graph parameters and length controls (&&&1&&&). In the customer support context, the pipeline implements a real-time incremental bullet-note generator with an agent-feedback loop to incorporate real-world corrections (Wu et al., 8 Oct 2025).

2. Core Interactive Components

The interactive backbone of SummPilot comprises several tightly-coupled components (Yun et al., 13 Jan 2026):

• Semantic Graph Construction: Extracts relational triples $$\langle \text{subject}, \text{relation}, \text{object}\rangle$$ via LLM prompts, forming a directed graph $(V, E)$ of entities and relations. Users can include or exclude subgraphs to directly influence content in future summary drafts.
• Entity Clustering: Performs LLM-based coreference resolution, clustering mentions referring to the same real-world entity. Formally, mentions $M$ are partitioned into clusters $\{C_1, \ldots, C_k\}$, and a canonical mention $\hat m_j$ is selected per cluster.
• Explainable Evaluation: Computes three server-side metrics per summary:
  • Compression: $\mathrm{Compression} = 1 - |S|/|D|$ (ratio of summary to source token counts),
  • Coverage (Newsroom-style): Proportion of source n-grams (n=1 to 4) covered by the summary,
  • Factual Consistency (FactScore): Each atomic fact derived from the summary is automatically judged True/False against the source and flagged if unverified.

Users can view flagged facts, compression ratios, and content coverage for each candidate summary, supporting iterative quality control.

3. Scientific SummPilot: Unsupervised Pipeline and Mathematical Formulation

In scientific paper summarization, SummPilot is instantiated as an unsupervised, graph-based pipeline adapted from SciSummPip (Ju et al., 2020). The workflow is:

1. Parsing and Sentence Segmentation: Papers are parsed (Science-Parse) and segmented into sentences.
2. Sentence Encoding: Each sentence $s_i$ is encoded using SciBERT, yielding embeddings $e_i$.
3. PageRank-based Content Selection: Pairwise similarity $w_{ij}$ (cosine of $e_i$, $e_j$) forms a complete matrix for PageRank; bottom $r$ fraction of low-ranked sentences are pruned.
4. Graph Construction and Spectral Clustering: Remaining sentences are connected via linguistic rules (coreference, discourse), clustered spectrally into $k$ groups.
5. Cluster Compression: Each cluster is compressed into a summary sentence via a word-graph, optimizing key-phrase and discourse coverage.
6. Length Constraint Enforcement: The number of clusters $k$ and per-cluster sentence compression ensure the final summary does not exceed a maximum length $L$.

PageRank for content selection is given by:

$$PR(v_i) = (1-d)/n + d \sum_{v_j \in In(i)} \frac{w_{ji}\,PR(v_j)}{\sum_{v_k \in Out(j)} w_{jk}}$$

Evaluations are performed with ROUGE and BERTScore. The SciSummPip-adapted SummPilot achieves ROUGE-1 F1 up to 47.37 on blind test sets, and BERTScore $F_1\approx0.815$ (Ju et al., 2020). Critical adaptations versus news-domain summarizers include replacement of embedding models (Word2Vec $\rightarrow$ SciBERT), aggressive filtering of background sentences (via cutoff ratio $r$), and summary-length controls tailored for scientific discourse.

4. Incremental Summarization and Feedback Integration

A related but distinct SummPilot variant targets real-time summarization in customer support systems (Wu et al., 8 Oct 2025). Its architecture consists of:

• Streaming LLM Bullet-notes: Incremental bullet points are generated in near real-time by a fine-tuned Mixtral-8×7B LLM at each message turn, prompted with interaction history and prior agent-validated bullets.
• DeBERTa Relevance Filtering: Each candidate bullet is classified for relevance, with a 12-layer DeBERTa (AUC=0.96, F1=0.895). Threshold $\tau=0.5$.
• Dual-Path Agent Feedback: Agent edits are injected in real-time for prompt history; all “before-edit” vs “after-edit” corrections are harvested for weekly offline LLM retraining using Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO).

The main objective combines loss on SFT-generated summaries and preference-tuning feedback:

$L_{total} = L_{generation} + \lambda L_{feedback}$

with $\lambda = 0.1$.

In production, this pipeline yielded a 3–9% reduction in case-handling time (depending on complexity), summary length reduced by 26.5%, and improved agent-reported satisfaction versus post-hoc summarization (Wu et al., 8 Oct 2025).

5. Foundational Algorithmic and Evaluation Techniques

SummPilot systems consistently integrate advanced methodologies for both content extraction and evaluation:

• Prompt Engineering: Carefully designed, few-shot LLM prompts guide triple extraction, ensure concise generation, and minimize extraneous output (Yun et al., 13 Jan 2026).
• Graph and Cluster Manipulation: User-driven toggling of graph nodes or cluster memberships affords direct control over summary content.
• Explainability and Verification: Explainable metrics (compression, coverage, consistency) are prominently featured for transparent, user-auditable summary quality.
• Unsupervised Optimization: Graph-based approaches (PageRank, spectral clustering) and content filtering (cutoff ratio $r$) are critical for scalable, domain-adapted systems (Ju et al., 2020).

6. Implementation, Empirical Results, and Usability

Key implementation features of LLM-based SummPilot include a Python Flask backend, React frontend, server-side LLM API orchestration, and reuse of graph/cluster/fact outputs across multiple user interactions for low-latency workflows (Yun et al., 13 Jan 2026).

Summary of comparative empirical results across domains:

Domain	Core Model(s)	Highlighted Metric(s)	Key Results
LLM+Graph (2026)	GPT-4o, graph, facts	SUS (usability), task acc.	SUS mean 86.3; task acc. 90%; Advanced higher on comprehension (Yun et al., 13 Jan 2026)
Scientific Papers	SciBERT, PageRank	ROUGE, BERTScore	Blind R1=47.37, F1=0.815 (SciSummPip) (Ju et al., 2020)
Customer Support	Mixtral-8x7B, DeBERTa	Case-handling time, conciseness	Time ↓3-9%, conciseness ↑, satisfaction >95% (Wu et al., 8 Oct 2025)

A plausible implication is that the integrated approach, combining automatic LLM generations with structural and interactive refinement plus transparent evaluation, yields consistently high usability, factual quality, and adaptation to varied user requirements.

7. Related Systems and Future Directions

SummPilot builds directly on several prior lines, including graph-ranking (e.g., SummPip, SciSummPip), sentence-scoring frameworks such as Quick Summary (Wahlstedt, 2012), and full-spectrum pilot/agent scheduling approaches (distinct “Pilot” abstraction in HPC, (Turilli et al., 2019)). Notably, SummPilot architectures differ from pure extraction- or compression-centric summarizers by foregrounding deep user interaction, LLM-based coreference, and explainable feedback at every step.

Potential extensions identified in production deployments include dynamic classifier thresholding, support for multi-modal prompts (e.g., images, voice), and further automation in continuous retraining based on user feedback (Wu et al., 8 Oct 2025).

SummPilot, in all its instantiations, represents a multi-faceted advancement in adaptive, efficient, and auditable summarization for both traditional and real-time textual domains.