arXiv (All)
open access
MAPLE: Medical Aspect-Based Summarization with Phrase-Level Evidence
Trustworthy clinical summarization requires every claim to be traceable to its evidence, yet existing attribution often resolves only to the sentence or document, leaving clinicians to scan surrounding text for the few words that matter. We argue that the unit of attribution should match the unit of
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- LexLattice: Multilingual Extractive Summarization via Neural Cellular Automata on Document Hierarchies#1063243
- Single Document Extractive Summarization using Domination in Hypergraph#1110391
- Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation#609622
- Decoding viral protein sequences by large language models.#382569
- Combining document structure and language model for IMRaD classification#1158399
Record · ID 1116188
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive