Capturing the Content of a Document through Complex Event Identification (2022)

Abstract: Granular events, instantiated in a document by predicates, can usually be grouped into more general events, called complex events. Together, they capture the major content of the document. Recent work grouped granular events by defining event regions, filtering out sentences that are irrelevant to the main content. However, this approach assumes that a given complex event is always described in consecutive sentences, which does not always hold in practice. In this paper, we introduce the task of complex event identification. We address this task as a pipeline, first predicting whether two granular events mentioned in the text belong to the same complex event, independently of their position in the text, and then using this to cluster them into complex events. Due to the difficulty of predicting whether two granular events belong to the same complex event in isolation, we propose a context-augmented representation learning approach CONTEXTRL that adds additional context to better model the pairwise relation between granular events. We show that our approach outperforms strong baselines on the complex event identification task and further present a promising case study exploring the effectiveness of using complex events as input for document-level argument extraction.

## introduction event extraction aims to identify event predicates and arguments from text and then identify their types and roles respectively, helping humans to easily understand the events. it has attracted considerable interest in the last few years @xcite @xcite @xcite @xcite due to the vast amounts of unstructured text available in domains like e-commerce, healthcare and industry. however, considering each five protesters were (e2:killed) when they were (e3:attacked) by an armed group. the armed group (e5:attacked) the demonstrators who have for days been staging their (e7:protest) against the military government. many protesters are supporters of an ultraconservative islamist candidate in (e8:elections) who was expelled from the election (e10:race) when it was (e11:discovered) that his mother held dual egyptian-u.s citizenship. the (e13:attack) on wednesday (e14:wounded) at least 50 protesters, and the attackers (e15:used) stones, sticks and molotov cocktails. granular event instantiated in the document by a predicate in isolation is not sufficient for understanding the entire context of the document. since granular events can be grouped into more general events, called complex events, we suggest using them to capture the major content of the document. a document could contain any number of complex events where each complex event contains more than one granular event. for example, figure 1 represents 10 granular events appearing in a document. one can group the granular events into two complex events as follows: (i) ce1 (in green) that includes the granular events related to a protest, (ii) ce2 (in red) that includes granular events that, taken together, describe elections. these two complex events represent the major two events that the text describes. recently, @xcite used the notion of event regions, a byproduct of document-level argument extraction, by filtering out sentences that are irrelevant to the main content and then parti-tioning the text into several parts. therefore, event regions are defined as consecutive sentences that include relevant arguments. however, compared to the complex event that groups related granular events together, the event region fails to capture the following two scenarios: (i) sentences that include granular events in the same complex event (e.g. the first and the last sentences in figure 1 ) are separated by sentences that include granular events in another complex event; (ii) two granular events belonging to different complex events may appear in the same sentence. therefore, in this paper, we introduce the task of complex event identification which aims to group granular events instantiated by predicates into complex events, independently from the position of the predicates in the text. for example, in figure 1 , e2 and e13 belong to the same complex event (ce1) while e8 belongs to ce2. to perform complex event identification, we first (i) predict whether two granular events belong to the same complex event, independently of their positions in the document, and then (ii) cluster them into complex events based on the pairwise relation predicted from step (i). however, only considering the joint representations of two granular events is not sufficient to model the pairwise relation. for example, in figure 1 , it is difficult to infer that "demonstrators have for days been staging their protest against the government" (e7) and "the attackers used stones, sticks and molotov cocktails" (e15) belong to the same complex event until we know that "the armed group attacked the demonstrators" (e5). moreover, since both "demonstrators have for days been staging their protest against the government" (e7) and "many protesters are supporters of a candidate in elections" (e8) mention some information about the protest, they might be considered to be in the same complex event. however, after reading more parts of the document, we know that e8 belong to the election complex event (ce2), which occurs before the protest complex event (ce1) containing e7. hence, we propose a context-augmented representation learning approach contextrl that adds additional context to model the pairwise complex event relation. specifically, we compute the attention distribution of other granular events in the document based on the joint representation of two granular events and select the one with the highest score as the context event. regarding two granular events as a single entity, if they belong to the same complex event, the system would add a granular event in the same complex event, to improve the expressiveness of their relatedness; if they are not in the same complex event, then the system would add an additional granular event to make them more distinguishable, relative to this context event. since there is not a dataset tailored to the task of complex event identification, we derive the complex event annotation from the hieve dataset @xcite that focuses on event-event relations. we show that our proposed approach outperforms strong baselines. moreover, since related granular events are grouped into the same complex event, the scope of the complex event is supposed to include all the information required for the prediction of the arguments of its granular events. hence, we conduct a case study on the wikievents dataset @xcite to explore the effectiveness of using complex events as the input for the document-level argument extraction task. we show that, when enough granular events are annotated, using complex events as input filters out noisy and irrelevant information, motivating the model to only focus on the related granular events. the major contributions of this paper can be summarized as follows: we also analyze the effect of the context event. ## method in this section, we present our context-augmented learning approach contextrl. we address the complex event identification task as a pipeline, first predicting whether two granular events belong to the same complex event, independently of their position in the text, and then grouping them into complex events based on pairwise predictions. we first introduce our pairwise complex event relation extraction model in section 3.1 and then introduce the granular event clustering step in section 3.2. ## experiments and results we conduct experiments on the complex event identification task, using our context-augmented representation learning approach contextrl to first extract pairwise relations and then group granular events into complex events through agglomerative clustering. we further present a promising case study on the wikievents dataset @xcite , showing the effectiveness of using only complex events as input for document-level argument extraction in section 4.6. ## conclusion in this work, we introduce the task of complex event identification and present a contextaugmented approach contextrl tailored to this task. we show that our approach outperforms strong baselines on the annotation derived from the hieve dataset and analyze positive effects of the context event. we further show the potential usefulness of using complex events as input for document-level argument extraction. for future work, we plan to directly annotate complex events from scratch with fine-grained guidelines. we also seek to extend our approach towards an end-to-end system with granular event extraction.