Query-Oriented Dialogue Summarization

Updated 7 July 2025

Query-Oriented Dialogue Summarization is the process of generating concise summaries that directly answer specific user queries by focusing on the most relevant dialogue segments.
It utilizes advanced methodologies including multi-view sequence-to-sequence models and retrieval-augmented pipelines to condition summary outputs on explicit query needs.
This approach improves applications in meeting analysis, customer service, and conversational AI while addressing challenges like information scattering and factual consistency.

Query-Oriented Dialogue Summarization is the task of generating a concise, focused summary of a dialogue or conversation that directly addresses a specific user query. Unlike generic summarization, which aims to capture all salient information, the query-oriented paradigm conditions the summary on explicit information needs, extracting and abstracting the segments of a multi-party, multi-turn dialogue most relevant to the query. This field integrates advances in dialogue understanding, controllable text generation, and fine-grained information retrieval, reflecting both technical complexity and high practical value for applications in meeting analysis, customer service, conversational AI, and beyond.

1. Problem Definition and Task Formulation

Formally, query-oriented dialogue summarization is defined as generating a summary $Y^Q$ given a dialogue $D$ and a query $Q$ , maximizing the conditional likelihood:

$Y^Q = \arg\max_{Y} P(Y|D, Q)$

The dialogue $D$ may span a wide range of genres—meetings, chats, technical support—and queries $Q$ may be general (“Summarize this discussion”) or aspect-specific (“What action points were agreed upon?”). Datasets such as QMSum structure their annotations around such queries, offering both overall and specific query types across meeting domains (2507.02145).

Distinct from document or conversation-level summarization, this task poses unique challenges: relevant information may be scattered, latent, or entwined with discourse or pragmatic context; salience must be query-conditioned; and the system must minimize irrelevant details while preserving coherence.

2. Model Architectures and Methodologies

Multi-View and Conversational Structure Models

Advances in dialogue structure modeling have played a central role in query-oriented tasks. The multi-view sequence-to-sequence framework, for example, first segments dialogues into topical or stage-based “views” by clustering utterance embeddings or employing Hidden Markov Models, and encodes each view separately (2010.01672). Decoding then employs view-specific and global attention mechanisms, dynamically aggregating evidence from views most relevant to the query at each generation step.

For finer control, sentence-gated mechanisms introduced in (1809.05715) explicitly model relationships between dialogue acts and summary content. Such a mechanism can be extended to query-oriented settings by incorporating query embeddings into the gating function, aligning information from dialogue acts, dialogue content, and query signals:

$g_\text{query} = \Sigma [v \cdot \tanh(c_i^\text{DA} + W_1 \cdot c^S + W_2 \cdot q)]$

where $g_\text{query}$ governs the flow of dialogue act information, $c^S$ is the summary context, and $q$ the query representation.

Instruction-Tuned and Retrieval-Augmented Models

Instruction tuning and prompt-based learning have enabled explicit control over summarization according to diverse user queries. Systems such as InstructDS train on synthesized query-dialogue-summary (QDS) triples, letting a single model generate generic, role-oriented, or query-based summaries conditioned on the prompt (2310.10981). A three-step pipeline (summary-anchored query generation, query filtering, query-based summary synthesis) enables training at scale with diverse query types, greatly expanding coverage and adaptability.

Retrieve-then-summarize pipelines address long and complex dialogues by first matching dialogue spans relevant to the query via embedding similarity or keyword matching, then condensing those segments with an abstractive generator (2107.03175). This two-stage structure is particularly effective in meeting transcripts where information is highly dispersed.

Reasoning LLMs and Chain-of-Thought Architectures

Stepwise reasoning LLMs employing explicit Chain-of-Thought (CoT) prompting (e.g., DeepSeek-R1, OpenAI-o1) have been systematically evaluated for query-based summarization (2507.02145). Contrary to success in math and QA, reasoning LLMs in this context often generate verbose, factually inconsistent, or less focused summaries than their non-reasoning (end-to-end generative) counterparts. While reasoning traces may make intermediate thought processes explicit, they frequently dilute summary focus and conciseness, especially when queries are tightly scoped.

3. Datasets, Evaluation Protocols, and Metrics

Key benchmarks for query-oriented dialogue summarization include:

QMSum: Features meeting transcripts (ICSI, AMI, ISRC), spanning academic, committee, and product meetings, systematically paired with diverse query types (overall, aspect-specific, generic).
SAMSum, DialogSum, CSDS: Support both dialogue and role- or query-focused summaries, with varying length and formality (1911.12237, 2105.06762, 2108.13139).

Evaluation employs both classical and advanced metrics:

Surface Metrics: ROUGE-N, CHRF, BLEU, and ROUGE-L, measuring n-gram overlap between generated and human reference summaries.
Semantic Metrics: BERTScore, MoverScore, BARTScore, COMET, which compute contextual or cross-lingual semantic similarity.
Human (LLM) Rankings: Increasingly, model outputs are ranked by expert annotators or strong LLMs (e.g., GPT-4o, DeepSeek-V3) along Relevance, Consistency, Fluency, Coherence, and Overall Quality (2507.02145).
Task-Specific Metrics: For goal-oriented settings, Call Type Accuracy (CT-Acc) and named entity matching metrics evaluate whether summaries faithfully reflect task intents and preserve essential entities (2409.10070).

Recent studies highlight the gap between high ROUGE scores and human preference in dialogue summarization—model summaries may optimize for n-gram overlap but miss salient, query-relevant content or introduce factual errors (1911.12237).

4. Empirical Findings and Comparative Analyses

Systematic evaluations using QMSum and related datasets have produced several robust findings:

Non-Reasoning LLMs such as GPT-4o and DeepSeek-V3 consistently outperform reasoning LLMs on query-focused dialogue summarization in both automatic and human-inspired metrics (2507.02145). Non-reasoning models generate more concise, directly focused outputs.
Reasoning LLMs tend to produce verbose, lower-coverage summaries with increased abstraction and novelty but reduced alignment to the posed query, particularly on “specific” queries where precision is critical.
Memory and Pointer-Generator Architectures may not consistently outperform simpler seq2seq models in complex, multi-party dialogues unless tailored carefully. The reproducibility of gains in different settings or with diverse annotator pools remains a challenge (2410.15962).
Data Augmentation and Pretraining (e.g., via template-based summaries, self-supervised signal, or pseudo-paraphrasing) improve robustness, especially where annotated query-based data is limited (2203.01552, 2204.13498, 2212.09750).

5. Practical Considerations and Deployment Strategies

Real-world deployment scenarios—meeting assistants, customer service analytics—demand both cost-efficiency and controlled output:

Multi-Query Optimization: Combining several queries for the same transcript into a single prompt can dramatically reduce LLM inference costs. Models like GPT-4 handle JSON-structured, multi-query instructions robustly, while open-source models often yield format errors, highlighting an important deployment consideration (2403.00067).
Semantic and Task Information Integration: Conditioning summarization models on SLU-derived semantic frames (e.g., intent, slot values) improves factual faithfulness and reduces entity hallucination in goal-oriented dialogue settings (2409.10070). Selection criteria based on KL divergence over task label distributions and named entity hallucination risk (NEHR) further refine summary fidelity.
Instruction Tuning for User Needs: Incorporating explicit instructions or user preferences (e.g., “summarize in 50 words,” “focus on decisions”) expands flexibility and user alignment, as demonstrated by instruction-tuned models such as InstructDS (2310.10981).

6. Challenges, Limitations, and Future Directions

Despite substantial progress, several challenges endure:

Data Scarcity and Annotation Cost: Rich, diverse QDS datasets remain limited. Synthesis pipelines (query generation, filtering, summary generation) partially address this but may introduce selection bias.
Evaluation Gaps: Existing metrics only weakly correlate with user-centric quality and factuality, especially in multi-party settings with distributed information. More reliable, task-aware evaluation frameworks are needed.
Factual Consistency: Reasoning LLMs and aggressive abstraction frequently introduce hallucinations or redundancy, suggesting the need for hybrid approaches that balance interpretability, conciseness, and faithfulness (2507.02145, 2111.03284).
Domain and Multilingual Robustness: Scaling to unseen domains and low-resource languages, integrating multimodal data (audio, video), and adapting to noisy ASR transcriptions are open technical problems (2212.10018, 2409.10070).
Model Efficiency and Scalability: Efficient inference (e.g., via prompt engineering for multi-query prompts) and robust format control are essential for production.

Promising research avenues include automatic or hybrid template generation methods for DST-style summarization (2203.01552), task-informed RL fine-tuning with human-in-the-loop reward signals (2212.09750), and the development of “hybrid” systems blending explicit reasoning with output post-processing to ensure brevity and query relevance.

In summary, query-oriented dialogue summarization integrates advances in dialogue structure modeling, instruction tuning, and controlled text generation to deliver user-centric, focused summaries. While models have made strides in aligning outputs with explicit information needs, challenges in factual consistency, evaluation, and robustness remain, motivating ongoing methodological innovation and empirical investigation.