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

Large-scale industrial recommender systems are usually confronted with
computational problems due to the enormous corpus size. To retrieve and
recommend the most relevant items to users under response time limits,
resorting to an efficient index structure is an effective and practical
solution. The previous work Tree-based Deep Model (TDM) \cite{zhu2018learning}
greatly improves recommendation accuracy using tree index. By indexing items in
a tree hierarchy and training a user-node preference prediction model
satisfying a max-heap like property in the tree, TDM provides logarithmic
computational complexity w.r.t. the corpus size, enabling the use of arbitrary
advanced models in candidate retrieval and recommendation.
In tree-based recommendation methods, the quality of both the tree index and
the user-node preference prediction model determines the recommendation
accuracy for the most part. We argue that the learning of tree index and
preference model has interdependence. Our purpose, in this paper, is to develop
a method to jointly learn the index structure and user preference prediction
model. In our proposed joint optimization framework, the learning of index and
user preference prediction model are carried out under a unified performance
measure. Besides, we come up with a novel hierarchical user preference
representation utilizing the tree index hierarchy. Experimental evaluations
with two large-scale real-world datasets show that the proposed method improves
recommendation accuracy significantly. Online A/B test results at a display
advertising platform also demonstrate the effectiveness of the proposed method
in production environments.

本文提出了一种联合优化的框架来学习树形推荐模型的索引结构和用户偏好预测模型，利用树形结构提出了一种新的用户偏好表示方法，针对大规模实际数据进行了实验验证，并在显示广告平台的在线 A/B 测试中证明了方法的有效性。

Joint Optimization of Tree-based Index and Deep Model for Recommender  Systems

Querying uncertain data sets (represented as probability distributions)
presents many challenges due to the large amount of data involved and the
difficulties comparing uncertainty between distributions. The Earth Mover's
Distance (EMD) has increasingly been employed to compare uncertain data due to
its ability to effectively capture the differences between two distributions.
Computing the EMD entails finding a solution to the transportation problem,
which is computationally intensive. In this paper, we propose a new lower bound
to the EMD and an index structure to significantly improve the performance of
EMD based K-nearest neighbor (K-NN) queries on uncertain databases. We propose
a new lower bound to the EMD that approximates the EMD on a projection vector.
Each distribution is projected onto a vector and approximated by a normal
distribution, as well as an accompanying error term. We then represent each
normal as a point in a Hough transformed space. We then use the concept of
stochastic dominance to implement an efficient index structure in the
transformed space. We show that our method significantly decreases K-NN query
time on uncertain databases. The index structure also scales well with database
cardinality. It is well suited for heterogeneous data sets, helping to keep EMD
based queries tractable as uncertain data sets become larger and more complex.

本文提出了一种使用 Earth Mover's Distance (EMD) 的指数结构来更有效地查询不确定性数据集的办法，借助新的 EMD 下限方法和索引结构来实现 K 个最近邻 (K-NN) 查询，通过将每个分布映射到向量上并将每个正常表示为 Hough 变换空间中的一个点，得到有效的性能提升和查询时间降低。