Bridging real-world summarization and laboratory-based memory recall.

A fundamental question in psychology is how humans digest, retain, and reconstruct information. While this process has been extensively studied in laboratory-based memory recall tasks, we have missed opportunities to examine it under more naturalistic, real-world conditions. Summarization is a task that people routinely engage in every day, and it is also a long-standing challenge in the machine learning community. Rich data sets of human summaries from machine learning provide a valuable, untapped resource for cognitive science researchers. In this work, we analyze 45,220 human summaries drawn from three large data sets, including news articles, autobiographical stories, and Reddit posts, and compare them with a data set of narrative memory recall. We demonstrate a close relationship between human summarization and laboratory-based memory recall. While summarization operates at a higher level of abstraction than memory recall, the two tasks share deep similarities. The process of summarizing is fundamentally constrained by memory mechanisms, displaying well-known memory regularities, including primacy, recency, and temporal contiguity effects. Conversely, memory recall takes the form of summarization, prioritizing central and important information. Together, these findings bridge the real-world task of summarization and the rich literature on memory recall and lay the foundation for developing integrated theories of how humans digest and reconstruct information. (PsycInfo Database Record (c) 2026 APA, all rights reserved).