When solving ill-posed inverse problems, one often desires to explore the
space of potential solutions rather than be presented with a single plausible
reconstruction. Valuable insights into these feasible solutions and their
associated probabilities are embedded in the posterior distribution. However,
when confronted with data of high dimensionality (such as images), visualizing
this distribution becomes a formidable challenge, necessitating the application
of effective summarization techniques before user examination. In this work, we
introduce a new approach for visualizing posteriors across multiple levels of
granularity using tree-valued predictions. Our method predicts a tree-valued
hierarchical summarization of the posterior distribution for any input
measurement, in a single forward pass of a neural network. We showcase the
efficacy of our approach across diverse datasets and image restoration
challenges, highlighting its prowess in uncertainty quantification and
visualization. Our findings reveal that our method performs comparably to a
baseline that hierarchically clusters samples from a diffusion-based posterior
sampler, yet achieves this with orders of magnitude greater speed.

通过使用树值预测，我们介绍了一种新的方法来可视化多个层次的后验分布，展示了在不同数据集和图像恢复挑战中的有效性，并揭示了其在不确定性量化和可视化方面的实力。

基于前馈后验树的分层不确定性探索

Hierarchical Uncertainty Exploration via Feedforward Posterior Trees

In today's data and information-rich world, summarization techniques are
essential in harnessing vast text to extract key information and enhance
decision-making and efficiency. In particular, topic-focused summarization is
important due to its ability to tailor content to specific aspects of an
extended text. However, this usually requires extensive labelled datasets and
considerable computational power. This study introduces a novel method,
Augmented-Query Summarization (AQS), for topic-focused summarization without
the need for extensive labelled datasets, leveraging query augmentation and
hierarchical clustering. This approach facilitates the transferability of
machine learning models to the task of summarization, circumventing the need
for topic-specific training. Through real-world tests, our method demonstrates
the ability to generate relevant and accurate summaries, showing its potential
as a cost-effective solution in data-rich environments. This innovation paves
the way for broader application and accessibility in the field of topic-focused
summarization technology, offering a scalable, efficient method for
personalized content extraction.

这项研究介绍了一种新的方法，增强型查询摘要（AQS），用于针对主题的摘要，无需大量标记数据集，利用查询扩展和分层聚类。通过 real-world 测试，我们的方法展示了生成相关和准确的摘要的能力，显示其在数据丰富环境中作为一种经济高效的解决方案的潜力，为主题聚焦摘要技术领域中的更广泛应用和可访问性铺平了道路，提供了一种可扩展的、高效的个性化内容提取方法。

无标签主题聚焦摘要生成的查询增强

Label-Free Topic-Focused Summarization Using Query Augmentation

Forum discussion data differ in both structure and properties from generic
form of textual data such as news. Henceforth, summarization techniques should,
in turn, make use of such differences, and craft models that can benefit from
the structural nature of discussion data. In this work, we propose attending to
the beginning of a document, to improve the performance of extractive
summarization models when applied to forum discussion data. Evaluations
demonstrated that with the help of bidirectional attention mechanism, attending
to the beginning of a document (initial comment/post) in a discussion thread,
can introduce a consistent boost in ROUGE scores, as well as introducing a new
State Of The Art (SOTA) ROUGE scores on the forum discussions dataset.
Additionally, we explored whether this hypothesis is extendable to other
generic forms of textual data. We make use of the tendency of introducing
important information early in the text, by attending to the first few
sentences in generic textual data. Evaluations demonstrated that attending to
introductory sentences using bidirectional attention, improves the performance
of extractive summarization models when even applied to more generic form of
textual data.

本文提出使用双向注意机制，关注讨论帖子中的开头（初始评论 / 帖子），可以引入一致的 ROUGE 分数提升，并在论坛讨论数据集上引入新的 ROUGE 分数的最新技术，同时发现此假设是否可扩展到其他通用形式的文本数据，通过考虑文本中前几句话，关注开头句，使用双向注意机制可以让摘要提取模型在更通用的文本数据上性能得到提升。