PerSEval: Assessing Personalization in Text Summarizers

Understand

Personalized summarization models cater to individuals' subjective understanding of saliency, as represented by their reading history and current topics of attention.

• Existing personalized text summarizers are primarily evaluated based on accuracy measures such as BLEU, ROUGE, and METEOR.
• However, a recent study argued that accuracy measures are inadequate for evaluating the degree of personalization of these models and proposed EGISES, the first metric to evaluate personalized text summaries.
• It was suggested that accuracy is a separate aspect and should be evaluated standalone.