Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation
The majority of work on summarization evaluation focuses on general summary quality (e.g., ROUGE, BERTScore) or specific desired properties (e.g., readability, factuality). However, these metrics fail to measure the utility of a summary to an individual user. For example, a biomedical researcher learning about the latest vaccine research will have different informational needs from a family doctor. Query-focused summarization captures part of this need, but in practice, users rarely state everything relevant in a query: a single short query is likely inadequate to distinguish the needs of a re
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
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Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- LinkedLinked via arxiv author · 85%Isabel Cachola →
“Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation”
- LinkedLinked via arxiv author · 85%William Walden →
“Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation”
- LinkedLinked via arxiv author · 85%Reno Kriz →
“Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation”
- LinkedLinked via arxiv author · 85%Mark Dredze →
“Information Satisfaction: A Reader-Centered Axis for Summarization Evaluation”