Dialogue summarization aims to condense a given dialogue into a simple and
focused summary text. Typically, both the roles' viewpoints and conversational
topics change in the dialogue stream. Thus how to effectively handle the
shifting topics and select the most salient utterance becomes one of the major
challenges of this task. In this paper, we propose a novel topic-aware
Global-Local Centrality (GLC) model to help select the salient context from all
sub-topics. The centralities are constructed at both the global and local
levels. The global one aims to identify vital sub-topics in the dialogue and
the local one aims to select the most important context in each sub-topic.
Specifically, the GLC collects sub-topic based on the utterance
representations. And each utterance is aligned with one sub-topic. Based on the
sub-topics, the GLC calculates global- and local-level centralities. Finally,
we combine the two to guide the model to capture both salient context and
sub-topics when generating summaries. Experimental results show that our model
outperforms strong baselines on three public dialogue summarization datasets:
CSDS, MC, and SAMSUM. Further analysis demonstrates that our GLC can exactly
identify vital contents from
sub-topics.~\footnote{https://github.com/xnliang98/bart-glc}

这篇文章提出了一种新颖的主题感知的全局局部中心性（GLC）模型，该模型可以在对话中选择最显著的语境，并能够准确地从子主题中识别关键内容，实验结果表明，该模型在对话摘要数据集上的表现优于强基线。

利用主题感知的全局和局部中心性提升对话摘要

Enhancing Dialogue Summarization with Topic-Aware Global- and Local- Level Centrality

Fact verification is a challenging task that requires simultaneously
reasoning and aggregating over multiple retrieved pieces of evidence to
evaluate the truthfulness of a claim. Existing approaches typically (i) explore
the semantic interaction between the claim and evidence at different
granularity levels but fail to capture their topical consistency during the
reasoning process, which we believe is crucial for verification; (ii) aggregate
multiple pieces of evidence equally without considering their implicit stances
to the claim, thereby introducing spurious information. To alleviate the above
issues, we propose a novel topic-aware evidence reasoning and stance-aware
aggregation model for more accurate fact verification, with the following four
key properties: 1) checking topical consistency between the claim and evidence;
2) maintaining topical coherence among multiple pieces of evidence; 3) ensuring
semantic similarity between the global topic information and the semantic
representation of evidence; 4) aggregating evidence based on their implicit
stances to the claim. Extensive experiments conducted on the two benchmark
datasets demonstrate the superiority of the proposed model over several
state-of-the-art approaches for fact verification. The source code can be
obtained from this https URL

该研究提出了一种主题感知证据推理和立场感知集成模型来更准确地验证事实，重点在于检查索赔和证据之间的主题一致性，维护多个证据之间的主题连贯性，确保全局主题信息和证据的语义表示之间的语义相似性，并基于证据对索赔的隐含立场聚合证据。