Understanding Neural Abstractive Summarization Models via Uncertainty

TL;DR

This paper investigates the uncertainty in neural abstractive summarization models, revealing how entropy relates to token copying, sentence position, and syntactic factors, and linking attention mechanisms to these uncertainties.

Contribution

It introduces an analysis of model uncertainty in summarization, connecting entropy to token copying behavior and attention, providing insights into model interpretability.

Findings

Low entropy correlates with token copying.

Uncertainty varies with sentence position and syntactic distance.

Attention mechanisms relate to observed uncertainty patterns.

Abstract

An advantage of seq2seq abstractive summarization models is that they generate text in a free-form manner, but this flexibility makes it difficult to interpret model behavior. In this work, we analyze summarization decoders in both blackbox and whitebox ways by studying on the entropy, or uncertainty, of the model's token-level predictions. For two strong pre-trained models, PEGASUS and BART on two summarization datasets, we find a strong correlation between low prediction entropy and where the model copies tokens rather than generating novel text. The decoder's uncertainty also connects to factors like sentence position and syntactic distance between adjacent pairs of tokens, giving a sense of what factors make a context particularly selective for the model's next output token. Finally, we study the relationship of decoder uncertainty and attention behavior to understand how attention gives rise to these observed effects in the model. We show that uncertainty is a useful perspective for analyzing summarization and text generation models more broadly.