EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain

TL;DR

This paper introduces SciEvents, a large-scale dataset for scientific event extraction, and proposes EXCEEDS, a novel grid-based framework that effectively captures dense nuggets and complex events in scientific texts.

Contribution

The paper presents the first comprehensive scientific event dataset and a new nugget-based grid modeling method, achieving state-of-the-art results in scientific event extraction.

Findings

SciEvents contains 2,508 documents and 24,381 events.

EXCEEDS outperforms existing methods on the SciEvents dataset.

Both dataset and framework are publicly available.

Abstract

It is crucial to understand a specific domain by events. Extensive event extraction research has been conducted in many domains such as news, finance, and biology. However, event extraction in scientific domain is still insufficiently supported by comprehensive datasets and tailored methods. Compared with other domains, scientific domain has two characteristics: (1) denser nuggets and events, and (2) more complex information forms. To solve the above problem, considering these two characteristics, we first construct SciEvents, a large-scale multi-event document-level dataset with a schema tailored for scientific domain. It consists of 2,508 documents and 24,381 events under multi-stage manual annotation and quality control. Then, we propose EXCEEDS, an end-to-end scientific event extraction framework by encoding dense nuggets into a grid matrix and simplifying complex event extraction as a nugget-based grid modeling task. Experiments on SciEvents demonstrate state-of-the-art performances of EXCEEDS. Both the SciEvents dataset and the EXCEEDS framework are released publicly to facilitate future research.