Summarization as Indirect Supervision for Relation Extraction
This work addresses the annotation bottleneck for relation extraction models, offering a novel approach that is incremental in leveraging existing summarization tasks.
The paper tackles the problem of expensive annotation for relation extraction (RE) by proposing SuRE, which reformulates RE as a summarization task to leverage indirect supervision, resulting in improved precision and resource efficiency in experiments on three datasets.
Relation extraction (RE) models have been challenged by their reliance on training data with expensive annotations. Considering that summarization tasks aim at acquiring concise expressions of synoptical information from the longer context, these tasks naturally align with the objective of RE, i.e., extracting a kind of synoptical information that describes the relation of entity mentions. We present SuRE, which converts RE into a summarization formulation. SuRE leads to more precise and resource-efficient RE based on indirect supervision from summarization tasks. To achieve this goal, we develop sentence and relation conversion techniques that essentially bridge the formulation of summarization and RE tasks. We also incorporate constraint decoding techniques with Trie scoring to further enhance summarization-based RE with robust inference. Experiments on three RE datasets demonstrate the effectiveness of SuRE in both full-dataset and low-resource settings, showing that summarization is a promising source of indirect supervision to improve RE models.