  Document-level entity-based extraction (EE), aiming at extracting
entity-centric information such as entity roles and entity relations, is key to
automatic knowledge acquisition from text corpora for various domains. Most
document-level EE systems build extractive models, which struggle to model
long-term dependencies among entities at the document level. To address this
issue, we propose a generative framework for two document-level EE tasks:
role-filler entity extraction (REE) and relation extraction (RE). We first
formulate them as a template generation problem, allowing models to efficiently
capture cross-entity dependencies, exploit label semantics, and avoid the
exponential computation complexity of identifying N-ary relations. A novel
cross-attention guided copy mechanism, TopK Copy, is incorporated into a
pre-trained sequence-to-sequence model to enhance the capabilities of
identifying key information in the input document. Experiments done on the
MUC-4 and SciREX dataset show new state-of-the-art results on REE (+3.26%),
binary RE (+4.8%), and 4-ary RE (+2.7%) in F1 score.

Computation and Language