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 more coherent,
detailed and accurate summaries that are significantly preferred by annotators
compared to prior work.

我们提出了一种无监督的抽象意见总结方法，将提取性方法的可归因性和可扩展性与大型语言模型（LLMs）的连贯性和流畅性相结合。

检索增强的意见摘要的分层索引

Hierarchical Indexing for Retrieval-Augmented Opinion Summarization

Text summarization aims to compress a textual document to a short summary
while keeping salient information. Extractive approaches are widely used in
text summarization because of their fluency and efficiency. However, most of
existing extractive models hardly capture inter-sentence relationships,
particularly in long documents. They also often ignore the effect of topical
information on capturing important contents. To address these issues, this
paper proposes a graph neural network (GNN)-based extractive summarization
model, enabling to capture inter-sentence relationships efficiently via
graph-structured document representation. Moreover, our model integrates a
joint neural topic model (NTM) to discover latent topics, which can provide
document-level features for sentence selection. The experimental results
demonstrate that our model not only substantially achieves state-of-the-art
results on CNN/DM and NYT datasets but also considerably outperforms existing
approaches on scientific paper datasets consisting of much longer documents,
indicating its better robustness in document genres and lengths. Further
discussions show that topical information can help the model preselect salient
contents from an entire document, which interprets its effectiveness in long
document summarization.

本文提出了基于图神经网络（GNN）的抽取式摘要模型，并整合了深度神经主题模型（NTM）来发现潜在主题，从而提供句子选择的文档级特征，实验结果表明，我们的模型在 CNN/DM 和 NYT 数据集上取得了最先进的结果，在由较长文档组成的科学论文数据集上也明显优于现有方法，进一步研究表明，主题信息可以帮助模型从整个文档中预先选择凸显内容，因此能够有效地摘要长文档。