GUM-SAGE: A Novel Dataset and Approach for Graded Entity Salience Prediction
This addresses the need for consistent and explainable entity salience prediction in user-facing systems, though it is incremental as it builds on prior methods.
The paper tackles the problem of predicting graded entity salience in texts by introducing a new dataset and method that combines subjective judgments and summarization-based approaches, achieving stronger correlation with human summaries and outperforming existing techniques including LLMs.
Determining and ranking the most salient entities in a text is critical for user-facing systems, especially as users increasingly rely on models to interpret long documents they only partially read. Graded entity salience addresses this need by assigning entities scores that reflect their relative importance in a text. Existing approaches fall into two main categories: subjective judgments of salience, which allow for gradient scoring but lack consistency, and summarization-based methods, which define salience as mention-worthiness in a summary, promoting explainability but limiting outputs to binary labels (entities are either summary-worthy or not). In this paper, we introduce a novel approach for graded entity salience that combines the strengths of both approaches. Using an English dataset spanning 12 spoken and written genres, we collect 5 summaries per document and calculate each entity's salience score based on its presence across these summaries. Our approach shows stronger correlation with scores based on human summaries and alignments, and outperforms existing techniques, including LLMs. We release our data and code at https://github.com/jl908069/gum_sum_salience to support further research on graded salient entity extraction.