用信息论框架为文本摘要设定性能下限，揭示真实模型差距。
A Rate-Distortion Framework for Summarization
- 基于率失真理论构建摘要性能的理论下界
- 提出迭代算法计算该下界，适用于有限数据场景
- 验证实际摘要模型性能与理论极限的差距
本文提出一种信息论框架用于文本摘要。定义了摘要生成器的率失真函数，并证明其提供了摘要性能的根本下界。我们设计了一种类似Blahut-Arimoto的迭代算法来计算该函数。为应对真实文本数据集的限制，还提出一种可在数据有限情况下计算该函数的实用方法。最后，通过将率失真函数与多种实际摘要模型的性能对比，实证验证了理论结果的正确性。
原文摘要 · Abstract (English)
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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