  We propose a method for unsupervised abstractive opinion summarization, that
combines the attributability and scalability of extractive approaches with the
coherence and fluency of Large Language Models (LLMs). Our method, HIRO, learns
an index structure that maps sentences to a path through a semantically
organized discrete hierarchy. At inference time, we populate the index and use
it to identify and retrieve clusters of sentences containing popular opinions
from input reviews. Then, we use a pretrained LLM to generate a readable
summary that is grounded in these extracted evidential clusters. The modularity
of our approach allows us to evaluate its efficacy at each stage. We show that
HIRO learns an encoding space that is more semantically structured than prior
work, and generates summaries that are more representative of the opinions in
the input reviews. Human evaluation confirms that HIRO generates significantly
more coherent, detailed and accurate summaries.
