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

A major drawback of modern neural OpenIE systems and benchmarks is that they
prioritize high coverage of information in extractions over compactness of
their constituents. This severely limits the usefulness of OpenIE extractions
in many downstream tasks. The utility of extractions can be improved if
extractions are compact and share constituents. To this end, we study the
problem of identifying compact extractions with neural-based methods. We
propose CompactIE, an OpenIE system that uses a novel pipelined approach to
produce compact extractions with overlapping constituents. It first detects
constituents of the extractions and then links them to build extractions. We
train our system on compact extractions obtained by processing existing
benchmarks. Our experiments on CaRB and Wire57 datasets indicate that CompactIE
finds 1.5x-2x more compact extractions than previous systems, with high
precision, establishing a new state-of-the-art performance in OpenIE.

研究致力于解决现代神经 OpenIE 系统在提取信息时追求高覆盖率而忽视紧凑性的问题，提出了使用新型管道方法生成具有重叠成分的紧凑产物的 OpenIE 系统 CompactIE，并通过处理现有基准测试数据获得紧凑的提取，实验表明它比之前的系统找到了 1.5x-2x 更多的紧凑提取，并确立了新的 OpenIE 性能最佳状态。