Summarizing comparative opinions about entities (e.g., hotels, phones) from a
set of source reviews, often referred to as contrastive summarization, can
considerably aid users in decision making. However, reliably measuring the
contrastiveness of the output summaries without relying on human evaluations
remains an open problem. Prior work has proposed token-overlap based metrics,
Distinctiveness Score, to measure contrast which does not take into account the
sensitivity to meaning-preserving lexical variations. In this work, we propose
an automated evaluation metric CASPR to better measure contrast between a pair
of summaries. Our metric is based on a simple and light-weight method that
leverages natural language inference (NLI) task to measure contrast by
segmenting reviews into single-claim sentences and carefully aggregating NLI
scores between them to come up with a summary-level score. We compare CASPR
with Distinctiveness Score and a simple yet powerful baseline based on
BERTScore. Our results on a prior dataset CoCoTRIP demonstrate that CASPR can
more reliably capture the contrastiveness of the summary pairs compared to the
baselines.

本文提出了一种基于自然语言推理（NLI）的自动评估指标 CASPR，用于更好地测量摘要对之间的对比度，并通过对比实验结果表明，CASPR 相比于其他基准方法能够更可靠地捕捉到摘要对之间的对比度。

CASPR：对比摘要的自动评估指标

CASPR: Automated Evaluation Metric for Contrastive Summarization

Tasks critical to enterprise profitability, such as customer churn
prediction, fraudulent account detection or customer lifetime value estimation,
are often tackled by models trained on features engineered from customer data
in tabular format. Application-specific feature engineering adds development,
operationalization and maintenance costs over time. Recent advances in
representation learning present an opportunity to simplify and generalize
feature engineering across applications. When applying these advancements to
tabular data researchers deal with data heterogeneity, variations in customer
engagement history or the sheer volume of enterprise datasets. In this paper,
we propose a novel approach to encode tabular data containing customer
transactions, purchase history and other interactions into a generic
representation of a customer's association with the business. We then evaluate
these embeddings as features to train multiple models spanning a variety of
applications. CASPR, Customer Activity Sequence-based Prediction and
Representation, applies Transformer architecture to encode activity sequences
to improve model performance and avoid bespoke feature engineering across
applications. Our experiments at scale validate CASPR for both small and large
enterprise applications.

本文提出了一种将客户数据编码为客户与企业关联的通用表示形式的新方法 CASPR，CASPR 应用转换器架构来编码活动序列以提高模型性能并避免应用程序间的特征工程。