EVESUM: Abstractive Multi‐Document Summarization Focused on Crucial Events

Abstract

Events with their triggers and entity mentions are significant in multi‐document summarization, which provide assistance to the recognition of the vital content of plenty of documents and guarantee the logic of the text. Many existing methods pay attention to leveraging fine‐grained and noun‐centered units like entities to interact with different documents rather than verb‐centered units, where the state changes and salient semantic associations in documents are neglected, causing a lack of logic in summarization. To address these issues, we propose an innovative abstractive multi‐document summarization model focused on events comprised of dynamic triggers and static entities, referred to as Event‐focused Abstractive Multi‐document Summarization (EVESUM), to form high‐quality summaries by explicitly introducing event information from documents. Specifically, we first apply an elaborately designed event‐focused graph attention network to fuse information from events and documents from different encoders, which particularly considers the function of edges in the graph. Additionally, we construct a hierarchical attention mechanism in the decoder to incorporate crucial event messages into summaries, making the generated summaries more succinct, informative, and logical. Experimental results illustrate that compared with state‐of‐the‐art baselines, our model achieves remarkable improvements on the WikiSum and MultiNews datasets.