With increasing volume of data being used across machine learning tasks, the
capability to target specific subsets of data becomes more important. To aid in
this capability, the recently proposed Submodular Mutual Information (SMI) has
been effectively applied across numerous tasks in literature to perform
targeted subset selection with the aid of a exemplar query set. However, all
such works are deficient in providing theoretical guarantees for SMI in terms
of its sensitivity to a subset's relevance and coverage of the targeted data.
For the first time, we provide such guarantees by deriving similarity-based
bounds on quantities related to relevance and coverage of the targeted data.
With these bounds, we show that the SMI functions, which have empirically shown
success in multiple applications, are theoretically sound in achieving good
query relevance and query coverage.

通过导出与相关性和目标数据的覆盖相关的数量的相似性为基础的上下界，我们首次提供了关于 Submodular Mutual Information（SMI）在子集的相关性和覆盖范围方面的理论保证，展示了 SMI 函数在实现良好的查询相关性和查询覆盖方面的理论上的可靠性。

针对数据子集选择的次模信息度量的理论分析

Theoretical Analysis of Submodular Information Measures for Targeted  Data Subset Selection

Although the problem of automatic video summarization has recently received a
lot of attention, the problem of creating a video summary that also highlights
elements relevant to a search query has been less studied. We address this
problem by posing query-relevant summarization as a video frame subset
selection problem, which lets us optimise for summaries which are
simultaneously diverse, representative of the entire video, and relevant to a
text query. We quantify relevance by measuring the distance between frames and
queries in a common textual-visual semantic embedding space induced by a neural
network. In addition, we extend the model to capture query-independent
properties, such as frame quality. We compare our method against previous state
of the art on textual-visual embeddings for thumbnail selection and show that
our model outperforms them on relevance prediction. Furthermore, we introduce a
new dataset, annotated with diversity and query-specific relevance labels. On
this dataset, we train and test our complete model for video summarization and
show that it outperforms standard baselines such as Maximal Marginal Relevance.

该研究利用神经网络诱导的文本视觉语义嵌入空间，将查询相关汇总作为视频帧子集选择问题进行提出，该方法在多个方面优于先前的技术，并引入具有多样性和查询特定相关性标签的新数据集进行模型训练和测试。

质量感知相关性估计的查询自适应视频摘要

Query-adaptive Video Summarization via Quality-aware Relevance  Estimation

We consider the problem of using sentence compression techniques to
facilitate query-focused multi-document summarization. We present a
sentence-compression-based framework for the task, and design a series of
learning-based compression models built on parse trees. An innovative beam
search decoder is proposed to efficiently find highly probable compressions.
Under this framework, we show how to integrate various indicative metrics such
as linguistic motivation and query relevance into the compression process by
deriving a novel formulation of a compression scoring function. Our best model
achieves statistically significant improvement over the state-of-the-art
systems on several metrics (e.g. 8.0% and 5.4% improvements in ROUGE-2
respectively) for the DUC 2006 and 2007 summarization task.

本文提出了一个基于句子压缩技术的任务框架，通过基于解析树的一系列学习模型，设计一种创新的 Beam Search 解码器来高效地找到高概率压缩结果，将语言学动机和查询相关性融入压缩过程中，并在多项指标上显著优于最先进的系统，包括在 DUC 2006 和 2007 摘要任务中分别获得 ROUGE-2 指标上 8.0% 和 5.4% 的改进。