  Relation extraction (RE) is a fundamental task in natural language
processing, aiming to identify relations between target entities in text. While
many RE methods are designed for a single sentence or document, cross-document
RE has emerged to address relations across multiple long documents. Given the
nature of long documents in cross-document RE, extracting document embeddings
is challenging due to the length constraints of pre-trained language models.
Therefore, we propose REward-based Input Construction (REIC), the first
learning-based sentence selector for cross-document RE. REIC extracts sentences
based on relational evidence, enabling the RE module to effectively infer
relations. Since supervision of evidence sentences is generally unavailable, we
train REIC using reinforcement learning with RE prediction scores as rewards.
Experimental results demonstrate the superiority of our method over heuristic
methods for different RE structures and backbones in cross-document RE. Our
code is publicly available at https://github.com/aailabkaist/REIC.
