Abstract: As a crucial task in the field of natural language processing,document-level relation extraction aims to accurately extract semantic relationships between entities from lengthy documents.Traditional document-level relation extraction methods ty-pically take the entire document as input.However,in reality,humans can predict relationships between entity pairs based on only a portion of the document,referred to as evidence sentences.In existing research,many methods start to utilize evidence sentences,but they face challenges such as incomplete evidence retrieval and difficulty in fully leveraging the advantages of these evidence sentences.To address this issue,we introduce a more efficient and accurate evidence sentence selection method.This is achieved by integrating a strategy for extracting evidence sentences through a fusion of formula-based and sentence-deletion-based approaches.We seamlessly integrate the evidence extraction with the training and inference processes,directing the document-le-vel relation extraction model to focus more on crucial sentences while still recognizing comprehensive information within the document.Experimental results demonstrate that the improved model outperforms existing models on public datasets.

Key words: Document-level, Relation extraction, Evidence sentences, Bilinear layer