Millions of news articles published online daily can overwhelm readers.
Headlines and entity (topic) tags are essential for guiding readers to decide
if the content is worth their time. While headline generation has been
extensively studied, tag generation remains largely unexplored, yet it offers
readers better access to topics of interest. The need for conciseness in
capturing readers' attention necessitates improved content selection strategies
for identifying salient and relevant segments within lengthy articles, thereby
guiding language models effectively. To address this, we propose to leverage
auxiliary information such as images and captions embedded in the articles to
retrieve relevant sentences and utilize instruction tuning with variations to
generate both headlines and tags for news articles in a multilingual context.
To make use of the auxiliary information, we have compiled a dataset named
XL-HeadTags, which includes 20 languages across 6 diverse language families.
Through extensive evaluation, we demonstrate the effectiveness of our
plug-and-play multimodal-multilingual retrievers for both tasks. Additionally,
we have developed a suite of tools for processing and evaluating multilingual
texts, significantly contributing to the research community by enabling more
accurate and efficient analysis across languages.

通过在多语言环境下利用辅助信息，如嵌入在文章中的图像和标题，检索相关句子并利用变化来生成新闻文章的标题和标签，该研究提出了一种用于头条和实体标签生成的方法，有效地引导阅读者对感兴趣的主题有更好的访问。

XL-HeadTags: 多模态检索增强的多语言新闻标题和标签生成

XL-HeadTags: Leveraging Multimodal Retrieval Augmentation for the  Multilingual Generation of News Headlines and Tags

A diversity of tasks use language models trained on semantic similarity data.
While there are a variety of datasets that capture semantic similarity, they
are either constructed from modern web data or are relatively small datasets
created in the past decade by human annotators. This study utilizes a novel
source, newly digitized articles from off-copyright, local U.S. newspapers, to
assemble a massive-scale semantic similarity dataset spanning 70 years from
1920 to 1989 and containing nearly 400M positive semantic similarity pairs.
Historically, around half of articles in U.S. local newspapers came from
newswires like the Associated Press. While local papers reproduced articles
from the newswire, they wrote their own headlines, which form abstractive
summaries of the associated articles. We associate articles and their headlines
by exploiting document layouts and language understanding. We then use deep
neural methods to detect which articles are from the same underlying source, in
the presence of substantial noise and abridgement. The headlines of reproduced
articles form positive semantic similarity pairs. The resulting publicly
available HEADLINES dataset is significantly larger than most existing semantic
similarity datasets and covers a much longer span of time. It will facilitate
the application of contrastively trained semantic similarity models to a
variety of tasks, including the study of semantic change across space and time.

该研究利用来自当地美国报纸的新数字化文章创建了一个巨大的语义相似性数据集，并利用深度神经方法检测了这些文章中的正面语义相似性对。该语义相似性数据集跨足了 70 年，包含近 400M 个正面语义相似性对，随着时间跨度的增加，将有助于将对比训练的语义相似性模型应用于各种任务。

历史英语语义相似性巨大数据集

A Massive Scale Semantic Similarity Dataset of Historical English

Newspapers are a popular form of written discourse, read by many people,
thanks to the novelty of the information provided by the news content in it. A
headline is the most widely read part of any newspaper due to its appearance in
a bigger font and sometimes in colour print. In this paper, we suggest and
implement a method for computing inferences from English news headlines,
excluding the information from the context in which the headlines appear. This
method attempts to generate the possible assumptions a reader formulates in
mind upon reading a fresh headline. The generated inferences could be useful
for assessing the impact of the news headline on readers including children.
The understandability of the current state of social affairs depends greatly on
the assimilation of the headlines. As the inferences that are independent of
the context depend mainly on the syntax of the headline, dependency trees of
headlines are used in this approach, to find the syntactical structure of the
headlines and to compute inferences out of them.

本文提出并实现了一种从英语新闻标题中计算推理的方法，利用标题的句法结构来计算与新闻背景无关的推断。这些推断可以有助于评估新闻标题对读者（包括孩子）的影响，了解社会状况的可理解度取决于标题的消化。