On the Role of Entity and Event Level Conceptualization in Generalizable Reasoning: A Survey of Tasks, Methods, Applications, and Future Directions (2025)

Abstract: Conceptualization, a fundamental element of human cognition, plays a pivotal role in human generalizable reasoning.Generally speaking, it refers to the process of sequentially abstracting specific instances into higher-level concepts and then forming abstract knowledge that can be applied in unfamiliar or novel situations. This enhances models’ inferential capabilities and supports the effective transfer of knowledge across various domains.Despite its significance, the broad nature of this term has led to inconsistencies in understanding conceptualization across various works, as there exists different types of instances that can be abstracted in a wide variety of ways.There is also a lack of a systematic overview that comprehensively examines existing works on the definition, execution, and application of conceptualization to enhance reasoning tasks.In this paper, we address these gaps by first proposing a categorization of different types of conceptualizations into four levels based on the types of instances being conceptualized, in order to clarify the term and define the scope of our work.Then, we present the first comprehensive survey of over 150 papers, surveying various definitions, resources, methods, and downstream applications related to conceptualization into a unified taxonomy, with a focus on the entity and event levels.Furthermore, we shed light on potential future directions in this field and hope to garner more attention from the community.

## four levels of conceptualization we first define four levels of conceptualization according to the type of instances being conceptualized. they are categorized into four levels: entity level, event level, document level, and system level. running examples are shown in figure 1 . entity level: entity-level conceptualization involves grouping multiple entities under a shared concept @xcite . it is the most common form of conceptualization in human cognition and is frequently applied for knowledge acquisition @xcite . for instance, entities like "apple," "pear," and "grape," can be categorized together under the broader concept of "fruit." by doing so, abstract knowledge can be derived by reintegrating the concept into the context of specific instances, such as the assertion "fruit is delicious," with "apple is delicious" serving as the specific source. when someone encounters an unknown fruit, they can quickly understand its properties by associating it with the abstract knowledge of fruit, such as its possible taste or nutrition. event level: while a concept can capture the semantic meaning of a group of entities, it can also represent events at a higher level of conceptualization. event-level conceptualization aims to broaden the scope from entities to include events as well @xcite . it seeks to associate different events under a shared concept that preserves the original semantic meaning to the maximum extent possible. for instance, activities like "sam playing with his dog," "alex dancing in the club," and "bob doing yoga" can all be conceptualized as "relaxing events." abstract knowledge can then follow, stating that "if someone engages in relaxing events, they feel happy and relaxed." when someone encounters an unknown 2020) yu et al. (2024); he et al. (2020); zhang et al. (2020, 2022 or unfamiliar event, such as "charlie likes painting the sunset," they can infer that painting the sunset is a relaxing event and that charlie feels happy and relaxed when doing so. document level: document-level conceptualization further extends the scope of the instance from entities and events to paragraphs or even entire documents. it aims to generate a summary that captures the main ideas and essential information while maintaining the overall semantic and context of the original text. previous works on abstractive summarization @xcite have identical objectives, and earlier surveys by @xcite ; @xcite ; @xcite have effectively summarized these studies. therefore, we only mention it here to clarify document-level conceptualization for readers and will not go into further detail in later sections to avoid overlap. system level: finally, system-level conceptualization aims to simplify the understanding of a complex system by abstracting its behavior and functionality into a higher-level representation. it is derived from the design of operating systems @xcite and is under-studied in the domain of nlp. the only representative example is a recent work by @xcite , where the authors provide a systematic categorization of nlp tasks based on their objectives and characteristics while neglecting the detailed format of input/output and the datasets on which the tasks are evaluated. due to the limited number of works available, we will not survey this type of conceptualization. in later sections, we focus specifically on entity and event-level conceptualizations and propose a taxonomy to categorize works into three categories. to ensure that our search for papers is comprehensive and objective in relation to our target scope, we propose the following three objectives for selecting the most relevant papers. first, we aim for papers that adhere to the paradigm of linking different instances together and use concepts as representations of the formed clusters. we also seek papers that aim to establish hierarchies between different entities and events. finally, we look for papers that directly seek abstractions of entities or events via concepts. our proposed taxonomy is primarily categorized into resources, methods, and downstream applications of conceptualization, as this is the most straightforward structure for readers to grasp the topic. ## tasks and datasets we first survey available datasets and benchmarks, as well as their associated tasks, for these two types of conceptualizations. statistical comparisons between different resources are shown in ## conceptualization acquisition methods next, we survey methods for performing or collecting entity and event-level conceptualizations. we categorize them into three paradigms: extraction, retrieval, and generative-based methods, which are briefly demonstrated in figure 3 . we provide more discussions in appendix a. ## downstream applications we then survey downstream tasks that can benefit from applying conceptualizations to provide readers with a general picture of what can be achieved and how to benefit from integrating conceptualizations. an overview of performances by different methods that leverage conceptualization, evaluated on various benchmarks, are shown in figure 4 . ## future directions and conclusions finally, we conclude our work by discussing two interesting future directions.