Deep Literature Survey Automation with an Iterative Workflow
TLDR
Proposes an iterative workflow for automated literature survey generation using recurrent outline generation and paper cards, outperforming baselines.
Reasoning
Strengths include a novel iterative approach that mimics human reading, paper cards for faithful grounding, and a new benchmark Survey-Arena. Weaknesses are that it focuses only on survey generation rather than full scientific discovery, and evaluation may still rely on subjective human judgment.
Read-first score
Read-first score 69.8, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 57.
Field roles
Rank sensitivity
Stability: volatile; rank range: 78.
Keyword Scores
Deep Analysis
Innovations
- Iterative workflow with recurrent outline generation where a planning agent incrementally retrieves, reads, and updates the outline
- Paper cards that distill each paper into contributions, methods, and findings for faithful paper-level grounding
- Review-and-refine loop with visualization enhancement to improve textual flow and integrate multimodal elements (figures, tables)
- Survey-Arena, a pairwise benchmark for more reliable assessment of machine-generated surveys relative to human-written ones
Methodology
The framework uses a planning agent that incrementally retrieves papers, reads them, and updates the outline iteratively. Paper cards summarize each paper's contributions, methods, and findings. A review-and-refine loop with visualization enhancement improves text and integrates figures/tables. Evaluation on established and emerging topics against state-of-the-art baselines using metrics of content coverage, structural coherence, and citation quality, plus a new pairwise benchmark Survey-Arena.
Key Results
The proposed method substantially outperforms state-of-the-art baselines in content coverage, structural coherence, and citation quality, producing more accessible and better-organized surveys.