Thinking about how to extract: Energizing LLM s’ emergence capabilities for document-level event argument extraction (2024)

Abstract: There are two key challenges remaining for the document-level event argument extraction (D-EAE) tasks: key feature forgetting and cross-event argument confusion. The emergence capability of large language models (LLMs) holds promise for solving the above two challenges. In this paper, we propose a document-level event argument extraction method based on guided summarization and reasoning (EAESR), which leverages the emergence capabilities of LLMs to highlight key event information and to clarify the explicit and implicit association between multiple events. Specifically, we generate document summarization information that shorten the length of the event context while preserving the key event features. In addition, we generate inter-event reasoning information, which helps EAESR make sense of the correlations between events and reduces their dependence on the event context, especially to better cope with the few-shot D-EAE task. Then, we obtain named entity information to enable EAESR to learn argument boundary features to improve the sensitivity of its argument boundary recognition. Eventually, we fused the above features and sentence features to make EAESR have summarizing and reasoning capabilities simultaneously. Extensive experiments on WIKIEVENTS and RAMS have shown that EAESR achieves a new state-of-the-art that outperforms the baseline models by 1.3% F1 and 1.6% F1, respectively, and averages 11% F1 in few-shot settings.

## introduction event argument extraction (eae), which is a critical task in event extraction (ee), can be divided into sentence-level eae (s-eae) and documentlevel eae (d-eae). as shown in figure 1 , s1 describes an injury event, s2 describes an identification event, etc., yet there are no events for s4 and * correponding author. figure 1 : example of d-eae. s1-s3 have events, and s4-s5 have no events. there are explicit and implicit associations between the arguments of the different events. s5 in the document. most existing d-eae models @xcite encode the entire document, which results in event-independent information interfering with event feature modeling, making it difficult to accurately identify events. in order to increase the proportion of key event information in the encoded features, some d-eae models @xcite set a fixed window to encode a portion of the document. its lack of global features of the document leads to its missed extraction of arguments scattered outside the window. the above two problems can be summarized as key feature forgetting in the d-eae task. on the other hand, as in figure 1 , "18:30 on may 14, 2014" is the time of both the injury event and identification event. yet the time of the confess event requires reasoning to learn that it is the day after the identification event. existing d-eae models @xcite @xcite construct the document as a heterogeneous graph so that they can obtain associations between tokens in the whole document. they will easily identify the explicit association of the cooccurrence of the argument "18:30 on may 14, 2014", while it is difficult to reason about the implicit association of "the following day", which may cause the d-eae model to incorrectly extract the time of the confess event as "18:30 on may 14, 2014". this phenomenon can be referred to as cross-event argument confusion in the d-eae task. recently, large language models (llms), like instructgpt @xcite , chatgpt foot_0 , and chatglm @xcite , utilize the instructions to work well in various downstream tasks such as conversations, summarization generation, etc. some works @xcite have also tried to design reasonable prompts and utilize chatgpt for event extraction with encouraging results. however, using llms directly for argument generation may cause serious precision problems due to the illusions of llms @xcite that can result in generating arguments outside of context. although it is not appropriate to apply llms directly for extracting arguments, we believe that the emergence capabilities of llms hold promise for d-eae models to model complex implicit associations in events, especially if they are used for event association analysis rather than argument generation. inspired by this idea, we propose a documentlevel event argument extraction method based on guided summarization and reasoning (eaesr), which utilizes llms to generate document summarization information and guidance descriptions for argument extraction as external supplementary features to address the key feature forgetting and cross-event argument confusion challenges of the d-eae task. specifically, for the key feature forgetting challenge, we first design the prompt for document summarization information generation to streamline the content of the document. as the structure between triggers and arguments in the event has a strong similarity with the structure between nodes and edges in the graph. we establish an abstract meaning representation (amr) graph for the document summarization information connecting the tokens in the summarization information. next, we use graph convolutional networks (gcn) to learn the weights of the edges in the amr graph, not only to obtain the global features of the document but also to establish co-occurrence associations of the event elements. for the cross-event argument confusion challenge, we design the prompt for reasoning information generation to analyze the explicit and implicit associations of inter-event arguments. we use llms to do a step-by-step analysis of the document's event associations and obtain the event reasoning features. through changing the learning objective from event features to event reasoning features, eaesr can accurately extract event arguments without relying on a large amount of training data. since most arguments in an event are named entities, we design prompts to extract entities from sentences to build entity features in order to improve the sensitivity of eaesr for recognizing argument boundaries. finally, we use the attention fusion layer combine the sentence features with the aforementioned features and obtain the event record. in summary, our proposed innovations and contributions are as follows: ## the proposed eaesr method as shown in figure 2 , eaesr contains three core components: summarizing feature extraction extracts the event key information in the document through llms. this allows eaesr to obtain the global semantics of the document while shortening the encoding length. reasoning feature extraction extracts the event argument extraction guidance description and entities through llms. this help eaesr to understand the explicit and implicit association of inter-event arguments and boundary features of the arguments. event argument extraction fuses the sentence features with the above features through the attention fusion layer, so that eaesr can address both of key feature forgetting and cross-event argument confusion challenges. ## conclusion and future works in this work, we propose a novel model of eaesr that can resolve key feature forgetting and crossevent argument confusion simultaneously. we utilize llms to generate external supplementary features related to events, including document global features, event reasoning features, and entity features. the document global features can provide eaesr with a global perspective and help it solve key feature forgetting. the event reasoning features and entity features can provide eaesr with explicit and implicit associations between events and help it solve cross-event argument confusion. extensive experiments on rams and wikievents demonstrate the effectiveness of our proposed model in the d-eae task. in future work, we will explore the use of llms to generate supplemental features for other information extraction tasks, such as event reasoning features for event relation extraction tasks.