MUDY: Multi-Granular Dynamic Candidate Contextualization for Unsupervised Keyphrase Extraction
> Paper: MUDY: Multi-Granular Dynamic Candidate Contextualization for Unsupervised Keyphrase Extraction > Authors: Hyeongu Kang, Susik Yoon > arXiv: 2605.00597 | 2026-04-30
The "Keyphrases That Aren't Key" Problem
Ask an AI to extract keyphrases from a paper structured as: (1) history of deep learning, (2) Transformer architecture, (3) a new attention mechanism, (4) NLP experiments. Typical methods return broad terms like "deep learning," "Transformer," and "NLP" — while missing the specific innovations ("new attention mechanism") and contributions ("experimental results"). Existing approaches excel at globally important phrases but ignore locally important ones.
Global vs. Local: The Blind Spot
- Global methods score candidates by semantic relevance to the entire document, so phrases like "deep learning" dominate even when a section's real contribution is, say, an improved dropout technique. Locally crucial phrases get low scores.
- Local methods capture position-specific importance but lack a global view, potentially extracting secondary details and fragmenting the keyphrase set.
- The solution requires multi-granular evaluation: both a global view and local sensitivity.
- Global: semantic similarity between the candidate and the whole document, computed with pretrained language models (document-level importance).
- Local: semantic association between the candidate and its surrounding sentences (paragraph-level importance). 2. Dynamic contextualization: scores are not static; they adjust based on where the candidate appears. The same phrase can matter differently in different positions. 3. Complementary fusion: global-high + local-high = core keyphrase; global-low + local-high = a key innovation specific to a section. Combining both yields fuller coverage.
- Global-only: keyphrases become generic ("deep learning" matters in every AI paper) and fail to distinguish actual contributions.
- Local-only: secondary details get picked up while core themes are lost.
- MUDY's multi-granularity gives hierarchical understanding (core themes plus specific contributions), complete coverage without drowning in global noise, and interpretability — you know whether a phrase was chosen for global importance, local importance, or both.
MUDY's Approach
Core idea: a keyphrase's importance depends on both global semantics and local context, and must be assessed at multiple granularities.
1. Dual-granularity scoring
It's like evaluating an employee by both overall contribution to the company and their pivotal role in a specific project — only together do they give a complete picture.
Why Multi-Granularity Matters
Takeaways
If you work on text analysis or information extraction, ask:
1. Does my method focus on only a single granularity? 2. Are global and local importance considered separately? 3. Could dynamic contextualization improve extraction quality? 4. Does multi-granular fusion fit my task?
MUDY reminds us that text understanding is hierarchical, not flat. The best keyphrase extraction "sees both the forest and the trees" — grasping global themes while capturing local highlights. Good extraction doesn't catch the biggest fish in the ocean of information; it catches the most representative ones, regardless of size.