Exploring Sentence Community for Document-Level Event Extraction (2021)

Abstract: Document-level event extraction is critical to various natural language processing tasks for providing structured information. Existing approaches by sequential modeling neglect the complex logic structures for long texts. In this paper, we leverage the entity interactions and sentence interactions within long documents and transform each document into an undirected unweighted graph by exploiting the relationship between sentences. We introduce the Sentence Community to represent each event as a subgraph. Furthermore, our framework SCDEE maintains the ability to extract multiple events by sentence community detection using graph attention networks and alleviate the role overlapping issue by predicting arguments in terms of roles. Experiments demonstrate that our framework achieves competitive results over state-of-the-art methods on the large-scale document-level event extraction dataset.

## introduction document-level event extraction (dee) aims to identify events in a long text with pre-specified types and corresponding event-specific argument roles. figure 1 illustrates an dee example for covid-19 tracking type with 5 arguments spreading across multiple sentences. generating document-level events is beneficial for a variety of natural language processing downstream tasks, such as knowledge base construction @xcite , article summarization @xcite , and question answering @xcite , since it can produce valuable structured information. however, the complex logic structures in long documents have made it a more challenging task than sentence-level event extraction (see) that extracts the event from the sentence. considering the above properties, in this paper, we propose to build document graphs based on these interactions and bring the document-level event extraction from sequential modeling to graphical document representation, which could be exploited to handle multiple problems in dee. specifically, we firstly propose a novel method that transforms each document into an undirected unweighted graph. each sentence presents one node considering the entity interaction, and we assign each node with a comprehensively encoded attribute vector based on bert @xcite . besides, the edges are constructed by entity cooccurrences between sentences in view of the sentence interaction. compared with sequential modeling, graph structure maintains the capability to drain the information from long-distance sentences to their related sentences through much fewer transitions. second, we propose the so-called sentence community to represent each event as a subgraph of the constructed document graph. specifically, we designate the sentence community by sentences that contain the arguments required for each event. in this way, the selected sentences also contain information about the corresponding event type. therefore, each sentence community contains all the information needed for the event. each sentence community corresponds to the related sentence nodes and edges in the document graph. third, we are able to mitigate the following issues based on our graphical representation: (1) multi-event issue. extracting multiple events for dee is challenging because of argument scattering and overlapping. 1 in the real world, long texts are prone to contain multiple events. to extract multiple events, we employ graph attention networks (gat) @xcite with the multi-head graph attention to detect overlapping sentence communities @xcite , then we classify event types and extract corresponding arguments with an entity-level attention mechanism for each sentence community. (2) role overlapping issue. an interesting problem in dee is role overlapping issue, which refers to the phenomenon that an argument can play multiple roles, and few attentions have been paid to the problem. for example, in sentence "on mar 3 2021, fedex pledges $2 billion toward sustainable energy initiatives", the "mar 3 2021" plays both the role "startdate" and the role "enddate" at the same time. we mitigate this issue by predicting arguments in terms of roles. in summary, our contributions include: ## methodology in this section, we present our proposed framework. we first introduce the document graph construction method. then we present the gnn-based sentence community detection approach. finally, we explain the event type and argument classification module. an overview is shown in figure 2 . ## conclusion in this paper, we propose a novel document-level event extraction framework that explores the sentence community for the event extraction task, which alleviates the multi-event issue and the role overlapping issue. in our framework, we introduce the document graph construction method that transforms a document into an undirected unweighted graph, which establishes associations between related sentences, and we employ the graph attention networks to capture the associations between sentences and further assign sentences to communities.