A Simple Theoretical Model of Importance for Summarization

TL;DR

This paper introduces a simple theoretical framework for importance in summarization, defining key concepts rigorously to unify redundancy, relevance, and informativeness, aiming to enhance understanding and development of summarization systems.

Contribution

It provides the first rigorous theoretical definitions of importance, redundancy, relevance, and informativeness in summarization, unifying these concepts into a single importance measure.

Findings

Framework offers intuitive interpretations of importance-related concepts.

Experiments demonstrate potential to guide future summarization research.

Abstract

Research on summarization has mainly been driven by empirical approaches, crafting systems to perform well on standard datasets with the notion of information Importance remaining latent. We argue that establishing theoretical models of Importance will advance our understanding of the task and help to further improve summarization systems. To this end, we propose simple but rigorous definitions of several concepts that were previously used only intuitively in summarization: Redundancy, Relevance, and Informativeness. Importance arises as a single quantity naturally unifying these concepts. Additionally, we provide intuitions to interpret the proposed quantities and experiments to demonstrate the potential of the framework to inform and guide subsequent works.