News Discourse Profiling seeks to scrutinize the event-related role of each
sentence in a news article and has been proven useful across various downstream
applications. Specifically, within the context of a given news discourse, each
sentence is assigned to a pre-defined category contingent upon its depiction of
the news event structure. However, existing approaches suffer from an
inadequacy of available human-annotated data, due to the laborious and
time-intensive nature of generating discourse-level annotations. In this paper,
we present a novel approach, denoted as Intra-document Contrastive Learning
with Distillation (ICLD), for addressing the news discourse profiling task,
capitalizing on its unique structural characteristics. Notably, we are the
first to apply a semi-supervised methodology within this task paradigm, and
evaluation demonstrates the effectiveness of the presented approach.

通过利用新闻报道的独特结构特征，我们提出了一种被称为 Intra-document Contrastive Learning with Distillation (ICLD) 的新方法，用于处理新闻话语分析任务，并首次在该任务范式中应用了半监督方法，评估结果显示该方法的有效性。

半监督式对比学习中的新闻话语建模

Semi-supervised News Discourse Profiling with Contrastive Learning

Rhetorical Structure Theory based Discourse Parsing (RST-DP) explores how
clauses, sentences, and large text spans compose a whole discourse and presents
the rhetorical structure as a hierarchical tree. Existing RST parsing pipelines
construct rhetorical structures without the knowledge of document-level content
structures, which causes relatively low performance when predicting the
discourse relations for large text spans. Recognizing the value of high-level
content-related information in facilitating discourse relation recognition, we
propose a novel pipeline for RST-DP that incorporates structure-aware news
content sentence representations derived from the task of News Discourse
Profiling. By incorporating only a few additional layers, this enhanced
pipeline exhibits promising performance across various RST parsing metrics.

基于修辞结构理论的话语解析（RST-DP）探索了子句、句子和大文本跨度如何组成整个话语，并将修辞结构呈现为一个分层树。现有的 RST 解析流水线在构建修辞结构时缺乏对文档级内容结构的了解，导致在预测大文本跨度的话语关系时性能相对较低。为了识别高级内容相关信息在促进话语关系识别方面的价值，我们提出了一种新颖的 RST-DP 流水线，该流水线结合了从新闻话语建模任务中得出的具有结构意识的新闻内容句子表示。通过仅添加了少量的附加层，这个增强的流水线在各种 RST 解析指标上表现出了很有前景的性能。

以文档级内容结构为导向的 RST 风格的篇章解析

RST-style Discourse Parsing Guided by Document-level Content Structures

We propose to leverage news discourse profiling to model document-level
temporal structures for building temporal dependency graphs. Our key
observation is that the functional roles of sentences used for profiling news
discourse signify different time frames relevant to a news story and can,
therefore, help to recover the global temporal structure of a document. Our
analyses and experiments with the widely used knowledge distillation technique
show that discourse profiling effectively identifies distant inter-sentence
event and (or) time expression pairs that are temporally related and otherwise
difficult to locate.

利用新闻话语建模文档级时间结构构建时间依赖图。通过新闻话语的功能角色可恢复文档的时间结构，利用知识蒸馏技术，有效识别时间上相关的远距离事件和时间表达式。