Nowadays, the use of intelligent systems to detect redundant information in
news articles has become especially prevalent with the proliferation of news
media outlets in order to enhance user experience. However, the heterogeneous
nature of news can lead to spurious findings in these systems: Simple
heuristics such as whether a pair of news are both about politics can provide
strong but deceptive downstream performance. Segmenting news similarity
datasets into topics improves the training of these models by forcing them to
learn how to distinguish salient characteristics under more narrow domains.
However, this requires the existence of topic-specific datasets, which are
currently lacking. In this article, we propose a new dataset of similar news,
SPICED, which includes seven topics: Crime & Law, Culture & Entertainment,
Disasters & Accidents, Economy & Business, Politics & Conflicts, Science &
Technology, and Sports. Futhermore, we present four distinct approaches for
generating news pairs, which are used in the creation of datasets specifically
designed for news similarity detection task. We benchmarked the created
datasets using MinHash, BERT, SBERT, and SimCSE models.

利用智能系统检测新闻文章中冗余信息的使用越来越普遍，而新闻媒体的激增为此提供了机会。然而，新闻的异质性可能导致这些系统产生虚假的发现。将新闻相似性数据集按主题分割能提高这些模型的训练效果，但目前缺乏主题特定的数据集。本文提出了一个新的相似新闻数据集 SPICED，包括犯罪与法律、文化与娱乐、灾害与事故、经济与商业、政治与冲突、科学与技术以及体育七个主题。此外，我们还提出了四种不同的方法用于生成新闻对，用于创建专门用于新闻相似性检测任务的数据集。我们使用 MinHash、BERT、SBERT 和 SimCSE 模型对创建的数据集进行了基准测试。

SPICED: 具有多个主题和复杂级别的新闻相似性检测数据集

SPICED: News Similarity Detection Dataset with Multiple Topics and  Complexity Levels

Much of the user-generated content on social media is provided by ordinary
people telling stories about their daily lives. We develop and test a novel
method for learning fine-grained common-sense knowledge from these stories
about contingent (causal and conditional) relationships between everyday
events. This type of knowledge is useful for text and story understanding,
information extraction, question answering, and text summarization. We test and
compare different methods for learning contingency relation, and compare what
is learned from topic-sorted story collections vs. general-domain stories. Our
experiments show that using topic-specific datasets enables learning
finer-grained knowledge about events and results in significant improvement
over the baselines. An evaluation on Amazon Mechanical Turk shows 82% of the
relations between events that we learn from topic-sorted stories are judged as
contingent.

通过普通人提供的社交媒体用户生成内容，我们开发并测试了一种新颖的方法，从中学习关于日常事件之间的因果和条件关系的细粒度常识知识，该方法对于文本和故事理解、信息提取、问题回答和文本摘要具有重要意义，并且我们的实验证明，使用特定主题数据集可以学习到更精细的事件知识，并且相对于基线得到了显着的改进。