Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models
*Forum summary of an arXiv paper (Aug 2024), originally posted on zhichai.net.*
Paper Information
| Field | Content | |------|------| | Title | Beyond Relevant Documents: A Knowledge-Intensive Approach for Query-Focused Summarization using Large Language Models | | Authors | Weijia Zhang, Jia-Hong Huang, Svitlana Vakulenko, Yumo Xu, Thilina Rajapakse, Evangelos Kanoulas | | Published | August 2024 (arXiv) | | Source | https://arxiv.org/abs/2408.10357 | | Category | Response Generation |
Background and Motivation
In large-scale search, recommendation, and personalization systems, information retrieval has long faced challenges around efficiency, scalability, and user intent understanding. Traditional pipeline approaches tend to separate retrieval, ranking, and generation, making it hard to meet the combined demands of natural language interaction, multi-hop reasoning, and up-to-date knowledge in the LLM era. This paper is proposed against that backdrop, focusing on query-focused summarization (QFS) that goes beyond simply collecting relevant documents toward knowledge-intensive summarization with large language models.
Core Contributions
- Proposes a knowledge-intensive approach to QFS using large language models, moving past the retrieve-relevant-documents paradigm.
- Decomposes the method into clear components (representation learning, retriever, reranker, planner, generator, feedback mechanisms) to ease engineering adoption.
- Discusses interfaces with emerging paradigms such as LLM tool calling, reinforcement learning, and multi-agent collaboration.
- Identifies open problems: evaluation credibility, latency and cost, hallucination and safety, and multilingual/multimodal extension.
- Datasets: MS MARCO, BEIR, Natural Questions, domain-specific corpora.
- Metrics: nDCG@10, MRR, Recall@k, Hit@k, human preference, task success rate, latency, and token cost.
- Neural headline generation: A comprehensive survey (Mar 2025, Neurocomputing)
- Cite Before You Speak: Enhancing Context-Response Grounding in E-commerce (arXiv:2503.04830)
- Improving Generative Ad Text on Facebook using Reinforcement Learning (arXiv:2507.21983)
- Original paper: https://arxiv.org/abs/2408.10357
Method / System Architecture (general pattern)
1. Input & representation: encode queries, documents, and user context into dense/sparse representations or structured prompts. 2. Core modules: retriever, reranker, planner, memory modules, tool interfaces, connected in series or parallel. 3. Learning strategy: supervised fine-tuning, contrastive learning, distillation, reinforcement learning (including process rewards), and synthetic data bootstrapping. 4. Inference strategy: single-round retrieval, iterative retrieval, parallel sub-queries, early stopping, and budget control.
Experiments and Evaluation (typical setup in this area)
Specific numerical results should be verified against the original PDF tables; this summary is based on the abstract and public metadata.
Key Takeaways for Search / Rec / Personalization
1. Architecture: cascaded retrieval + reranking + generation remains mainstream, but the agentic paradigm is making retrieval strategy itself learnable. 2. Data: high-quality instruction data and click/session logs are equally critical; synthetic data needs safeguards against knowledge leakage and distribution shift. 3. Evaluation: the gap between offline metrics and online satisfaction is widening; LLM-as-judge should be cross-validated with human assessment. 4. Product: latency, cost, interpretability, and safety policies are hard constraints for industrial deployment.
Limitations and Future Work
Potential limitations include experiment scale constrained by GPU budgets, mismatch between benchmarks and real user distributions, and unknown cross-lingual generalization from English-centric data. Future directions include more efficient test-time compute allocation, deeper integration with knowledge graphs and structured databases, and causal/fairness constraints for recommendation systems.