A Rate-Distortion Framework for Summarization

Enes Arda; Aylin Yener

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arXiv · 2501.13100

A Rate-Distortion Framework for Summarization

Enes Arda·Aylin Yener

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Abstract

This paper introduces an information-theoretic framework for text summarization. We define the summarizer rate-distortion function and show that it provides a fundamental lower bound on summarizer performance. We describe an iterative procedure, similar to Blahut-Arimoto algorithm, for computing this function. To handle real-world text datasets, we also propose a practical method that can calculate the summarizer rate-distortion function with limited data. Finally, we empirically confirm our theoretical results by comparing the summarizer rate-distortion function with the performances of different summarizers used in practice.

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Enes Arda, Aylin Yener. 2025-01-22. A Rate-Distortion Framework for Summarization. https://arxiv.org/abs/2501.13100

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