The exponential growth of textual data has created a crucial need for tools
that assist users in extracting meaningful insights. Traditional document
summarization approaches often fail to meet individual user requirements and
lack structure for efficient information processing. To address these
limitations, we propose Summation, a hierarchical personalized concept-based
summarization approach. It synthesizes documents into a concise hierarchical
concept map and actively engages users by learning and adapting to their
preferences. Using a Reinforcement Learning algorithm, Summation generates
personalized summaries for unseen documents on specific topics. This framework
enhances comprehension, enables effective navigation, and empowers users to
extract meaningful insights from large document collections aligned with their
unique requirements.

该论文提出了一种基于个性化概念的分层式摘要方法 Summation，使用强化学习算法生成特定主题的未见文档的个性化摘要，以增强理解、有效导航和满足用户独特需求从大量文献中提取有意义的见解。

一项用于从非结构化数据中学习结构的个性化强化学习摘要服务

A Personalized Reinforcement Learning Summarization Service for Learning  Structure from Unstructured Data

Recent work on the representation of functions on sets has considered the use
of summation in a latent space to enforce permutation invariance. In
particular, it has been conjectured that the dimension of this latent space may
remain fixed as the cardinality of the sets under consideration increases.
However, we demonstrate that the analysis leading to this conjecture requires
mappings which are highly discontinuous and argue that this is only of limited
practical use. Motivated by this observation, we prove that an implementation
of this model via continuous mappings (as provided by e.g. neural networks or
Gaussian processes) actually imposes a constraint on the dimensionality of the
latent space. Practical universal function representation for set inputs can
only be achieved with a latent dimension at least the size of the maximum
number of input elements.

本文研究函数在集合中的表示，借助于集合中求和的潜在空间来实现置换不变性。研究表明，基于离散映射实现的模型具有高度间断性，只有通过连续映射（如神经网络或高斯过程）实现的模型，才能对潜在空间的维度施加约束。为实现输入集合的通用函数表示，必须使潜在维度至少为输入元素的最大数量。