As NLP models become increasingly capable of understanding documents in terms
of coherent entities rather than strings, obtaining the most salient entities
for each document is not only an important end task in itself but also vital
for Information Retrieval (IR) and other downstream applications such as
controllable summarization. In this paper, we present and evaluate GUMsley, the
first entity salience dataset covering all named and non-named salient entities
for 12 genres of English text, aligned with entity types, Wikification links
and full coreference resolution annotations. We promote a strict definition of
salience using human summaries and demonstrate high inter-annotator agreement
for salience based on whether a source entity is mentioned in the summary. Our
evaluation shows poor performance by pre-trained SOTA summarization models and
zero-shot LLM prompting in capturing salient entities in generated summaries.
We also show that predicting or providing salient entities to several model
architectures enhances performance and helps derive higher-quality summaries by
alleviating the entity hallucination problem in existing abstractive
summarization.

该研究通过提供具有显著性的实体来改善摘要生成中的实体幻觉问题，并评估了预训练的最先进的摘要模型和零 - shot LLM 提示对于捕捉生成摘要中显著实体的能力。

GUMsley：对 12 种英语流派中摘要的实体显著性评估

GUMsley: Evaluating Entity Salience in Summarization for 12 English  Genres

This paper presents a Kernel Entity Salience Model (KESM) that improves text
understanding and retrieval by better estimating entity salience (importance)
in documents. KESM represents entities by knowledge enriched distributed
representations, models the interactions between entities and words by kernels,
and combines the kernel scores to estimate entity salience. The whole model is
learned end-to-end using entity salience labels. The salience model also
improves ad hoc search accuracy, providing effective ranking features by
modeling the salience of query entities in candidate documents. Our experiments
on two entity salience corpora and two TREC ad hoc search datasets demonstrate
the effectiveness of KESM over frequency-based and feature-based methods. We
also provide examples showing how KESM conveys its text understanding ability
learned from entity salience to search.

本文提出一种核实体显著性模型（KESM），通过更好地估计文档中实体的显著性（重要性），提高文本理解和检索。KESM 通过知识增强的分布式表示来表示实体，通过核函数建模实体和单词之间的交互，并结合核分数来估计实体显著性。该模型的整个过程都是通过学习实体显著性标签来完成的。显著性模型还通过模拟候选文档中查询实体的显著性，提供有效的排名特征，从而提高了即席搜索的准确性。我们在两个实体显著性语料库和两个 TREC 即席搜索数据集上的实验结果表明，KESM 比基于频率和特征的方法更为有效。我们还提供了示例，展示了 KESM 如何将其从实体显著性中学到的文本理解能力应用于搜索。

通过核心实体显著性建模实现更好的文本理解和检索

Towards Better Text Understanding and Retrieval through Kernel Entity  Salience Modeling

We study the problem of entity salience by proposing the design and
implementation of SWAT, a system that identifies the salient Wikipedia entities
occurring in an input document. SWAT consists of several modules that are able
to detect and classify on-the-fly Wikipedia entities as salient or not, based
on a large number of syntactic, semantic and latent features properly extracted
via a supervised process which has been trained over millions of examples drawn
from the New York Times corpus. The validation process is performed through a
large experimental assessment, eventually showing that SWAT improves known
solutions over all publicly available datasets. We release SWAT via an API that
we describe and comment in the paper in order to ease its use in other
software.

本文介绍了一个称为 SWAT 的系统，该系统通过监督过程检测和分类维基百科中的实体，最终使用 API 的方式公开。经过大量实验对比，证明 SWAT 在提高实体关注度方面比目前已有的公开数据集更为有效。