A Hybrid Approach and Unified Framework for Bibliographic Reference Extraction

TL;DR

This paper introduces DeepBiRD, a novel layout-based method for extracting references from scientific publications, supported by a large annotated dataset and integrated into a flexible framework, achieving high accuracy and generalization across styles.

Contribution

It presents a new layout-driven reference detection method, a large annotated dataset, and a unified framework for robust bibliographic reference extraction.

Findings

DeepBiRD achieved 98.56% AP50 on the new dataset.

Outperformed existing state-of-the-art methods.

Demonstrated strong generalization across datasets and styles.

Abstract

Publications are an integral part in a scientific community. Bibliographic reference extraction from scientific publication is a challenging task due to diversity in referencing styles and document layout. Existing methods perform sufficiently on one dataset however, applying these solutions to a different dataset proves to be challenging. Therefore, a generic solution was anticipated which could overcome the limitations of the previous approaches. The contribution of this paper is three-fold. First, it presents a novel approach called DeepBiRD which is inspired by human visual perception and exploits layout features to identify individual references in a scientific publication. Second, we release a large dataset for image-based reference detection with 2401 scans containing 38863 references, all manually annotated for individual reference. Third, we present a unified and highly configurable end-to-end automatic bibliographic reference extraction framework called BRExSys which employs DeepBiRD along with state-of-the-art text-based models to detect and visualize references from a bibliographic document. Our proposed approach pre-processes the images in which a hybrid representation is obtained by processing the given image using different computer vision techniques. Then, it performs layout driven reference detection using Mask R-CNN on a given scientific publication. DeepBiRD was evaluated on two different datasets to demonstrate the generalization of this approach. The proposed system achieved an AP50 of 98.56% on our dataset. DeepBiRD significantly outperformed the current state-of-the-art approach on their dataset. Therefore, suggesting that DeepBiRD is significantly superior in performance, generalized, and independent of any domain or referencing style.