  The principle of the Information Bottleneck (Tishby et al. 1999) is to
produce a summary of information X optimized to predict some other relevant
information Y. In this paper, we propose a novel approach to unsupervised
sentence summarization by mapping the Information Bottleneck principle to a
conditional language modelling objective: given a sentence, our approach seeks
a compressed sentence that can best predict the next sentence. Our iterative
algorithm under the Information Bottleneck objective searches gradually shorter
subsequences of the given sentence while maximizing the probability of the next
sentence conditioned on the summary. Using only pretrained language models with
no direct supervision, our approach can efficiently perform extractive sentence
summarization over a large corpus.
  Building on our unsupervised extractive summarization (BottleSumEx), we then
present a new approach to self-supervised abstractive summarization
(BottleSumSelf), where a transformer-based language model is trained on the
output summaries of our unsupervised method. Empirical results demonstrate that
our extractive method outperforms other unsupervised models on multiple
automatic metrics. In addition, we find that our self-supervised abstractive
model outperforms unsupervised baselines (including our own) by human
evaluation along multiple attributes.
