Storytelling, whether via fables, news reports, documentaries, or memoirs,
can be thought of as the communication of interesting and related events that,
taken together, form a concrete process. It is desirable to extract the event
chains that represent such processes. However, this extraction remains a
challenging problem. We posit that this is due to the nature of the texts from
which chains are discovered. Natural language text interleaves a narrative of
concrete, salient events with background information, contextualization,
opinion, and other elements that are important for a variety of necessary
discourse and pragmatics acts but are not part of the principal chain of events
being communicated. We introduce methods for extracting this principal chain
from natural language text, by filtering away non-salient events and supportive
sentences. We demonstrate the effectiveness of our methods at isolating
critical event chains by comparing their effect on downstream tasks. We show
that by pre-training large language models on our extracted chains, we obtain
improvements in two tasks that benefit from a clear understanding of event
chains: narrative prediction and event-based temporal question answering. The
demonstrated improvements and ablative studies confirm that our extraction
method isolates critical event chains.

本文提出了一种从自然语言文本中提取事件链的方法，该方法可以过滤非显著事件和支持性句子，并在两个任务中证明了其有效性：叙述预测和基于事件的时间问答。

基于突显感知的事件链建模用于叙事理解

Salience-Aware Event Chain Modeling for Narrative Understanding

We introduce a novel iterative approach for event coreference resolution that
gradually builds event clusters by exploiting inter-dependencies among event
mentions within the same chain as well as across event chains. Among event
mentions in the same chain, we distinguish within- and cross-document event
coreference links by using two distinct pairwise classifiers, trained
separately to capture differences in feature distributions of within- and
cross-document event clusters. Our event coreference approach alternates
between WD and CD clustering and combines arguments from both event clusters
after every merge, continuing till no more merge can be made. And then it
performs further merging between event chains that are both closely related to
a set of other chains of events. Experiments on the ECB+ corpus show that our
model outperforms state-of-the-art methods in joint task of WD and CD event
coreference resolution.

提出一种新的迭代方法，通过采用同一链中和跨事件链中的事件提及之间的相互依赖性逐渐构建事件聚类，并使用两个不同的成对分类器来区分同一链中和跨文档事件指称之间的事件指称，并交替进行聚类后合并，最终执行事件链之间的进一步合并，从而在 WD 和 CD 事件共指解析的联合任务中胜过现有方法。