Users often struggle with decision-making between two options (A vs B), as it
usually requires time-consuming research across multiple web pages. We propose
STRUM-LLM that addresses this challenge by generating attributed, structured,
and helpful contrastive summaries that highlight key differences between the
two options. STRUM-LLM identifies helpful contrast: the specific attributes
along which the two options differ significantly and which are most likely to
influence the user's decision. Our technique is domain-agnostic, and does not
require any human-labeled data or fixed attribute list as supervision.
STRUM-LLM attributes all extractions back to the input sources along with
textual evidence, and it does not have a limit on the length of input sources
that it can process. STRUM-LLM Distilled has 100x more throughput than the
models with comparable performance while being 10x smaller. In this paper, we
provide extensive evaluations for our method and lay out future directions for
our currently deployed system.

通过生成对比性摘要来解决用户在两个选项之间做决策时所面临的困难，STRUM-LLM 技术识别关键的差异属性，以及对用户决策最有影响力的特征。

STRUM-LLM：属性化和结构化的对比摘要

STRUM-LLM: Attributed and Structured Contrastive Summarization

Opinion summarization focuses on generating summaries that reflect popular
subjective information expressed in multiple online reviews. While generated
summaries offer general and concise information about a particular hotel or
product, the information may be insufficient to help the user compare multiple
different choices. Thus, the user may still struggle with the question "Which
one should I pick?" In this paper, we propose the comparative opinion
summarization task, which aims at generating two contrastive summaries and one
common summary from two different candidate sets of reviews. We develop a
comparative summarization framework CoCoSum, which consists of two base
summarization models that jointly generate contrastive and common summaries.
Experimental results on a newly created benchmark CoCoTrip show that CoCoSum
can produce higher-quality contrastive and common summaries than
state-of-the-art opinion summarization models. The dataset and code are
available at this https URL

该论文提出了比较意见摘要任务，即从两个不同的候选评论集中生成两个对比摘要和一个共同摘要。作者开发了一个比较摘要框架 CoCoSum，可以更好地生成对比和共同摘要。