  Document-level Relation Extraction (RE) requires extracting relations
expressed within and across sentences. Recent works show that graph-based
methods, usually constructing a document-level graph that captures
document-aware interactions, can obtain useful entity representations thus
helping tackle document-level RE. These methods either focus more on the entire
graph, or pay more attention to a part of the graph, e.g., paths between the
target entity pair. However, we find that document-level RE may benefit from
focusing on both of them simultaneously. Therefore, to obtain more
comprehensive entity representations, we propose the Coarse-to-Fine Entity
Representation model (CFER) that adopts a coarse-to-fine strategy involving two
phases. First, CFER uses graph neural networks to integrate global information
in the entire graph at a coarse level. Next, CFER utilizes the global
information as a guidance to selectively aggregate path information between the
target entity pair at a fine level. In classification, we combine the entity
representations from both two levels into more comprehensive representations
for relation extraction. Experimental results on two document-level RE
datasets, DocRED and CDR, show that CFER outperforms existing models and is
robust to the uneven label distribution.
