  Relation Extraction (RE) is a fundamental task of information extraction,
which has attracted a large amount of research attention. Previous studies
focus on extracting the relations within a sentence or document, while
currently researchers begin to explore cross-document RE. However, current
cross-document RE methods directly utilize text snippets surrounding target
entities in multiple given documents, which brings considerable noisy and
non-relevant sentences. Moreover, they utilize all the text paths in a document
bag in a coarse-grained way, without considering the connections between these
text paths.In this paper, we aim to address both of these shortages and push
the state-of-the-art for cross-document RE. First, we focus on input
construction for our RE model and propose an entity-based document-context
filter to retain useful information in the given documents by using the bridge
entities in the text paths. Second, we propose a cross-document RE model based
on cross-path entity relation attention, which allow the entity relations
across text paths to interact with each other. We compare our cross-document RE
method with the state-of-the-art methods in the dataset CodRED. Our method
outperforms them by at least 10% in F1, thus demonstrating its effectiveness.
