We present the Quantized Transformer (QT), an unsupervised system for
extractive opinion summarization. QT is inspired by Vector-Quantized
Variational Autoencoders, which we repurpose for popularity-driven
summarization. It uses a clustering interpretation of the quantized space and a
novel extraction algorithm to discover popular opinions among hundreds of
reviews, a significant step towards opinion summarization of practical scope.
In addition, QT enables controllable summarization without further training, by
utilizing properties of the quantized space to extract aspect-specific
summaries. We also make publicly available SPACE, a large-scale evaluation
benchmark for opinion summarizers, comprising general and aspect-specific
summaries for 50 hotels. Experiments demonstrate the promise of our approach,
which is validated by human studies where judges showed clear preference for
our method over competitive baselines.

本文介绍了一种基于量化转换器的无监督系统，用于提取意见摘要。由于采用聚类解释量化空间，加上一种新颖的提取算法，能够在数百个评论中发现受欢迎的意见，从而实现实用范围的意见摘要。此外，该方法利用量化空间的属性提取方面和细节方面的摘要，从而实现了可控摘要，无需进一步训练。作者也公开了 SPACE（一种大型评估基准）用于评估意见摘要，包括 50 个酒店的汇总。实验表明，作者的方法十分有前途，通过人工研究验证了该方法优于竞争基线的清晰偏好。

基于量化 Transformer 空间的提取式意见摘要

Extractive Opinion Summarization in Quantized Transformer Spaces

Semi-Non-negative Matrix Factorization is a technique that learns a
low-dimensional representation of a dataset that lends itself to a clustering
interpretation. It is possible that the mapping between this new representation
and our original data matrix contains rather complex hierarchical information
with implicit lower-level hidden attributes, that classical one level
clustering methodologies can not interpret. In this work we propose a novel
model, Deep Semi-NMF, that is able to learn such hidden representations that
allow themselves to an interpretation of clustering according to different,
unknown attributes of a given dataset. We also present a semi-supervised
version of the algorithm, named Deep WSF, that allows the use of (partial)
prior information for each of the known attributes of a dataset, that allows
the model to be used on datasets with mixed attribute knowledge. Finally, we
show that our models are able to learn low-dimensional representations that are
better suited for clustering, but also classification, outperforming
Semi-Non-negative Matrix Factorization, but also other state-of-the-art
methodologies variants.

本研究提出了一种新的模型 Deep Semi-NMF，可以将 Semi-Non-negative Matrix Factorization 中无法解释的低级隐藏属性进行聚类解释，并提供了 Deep WSF 的半监督版本，可用于处理不同属性的数据集，可以用于聚类和分类，其性能表现优于其他现有的方法。