[2406.14075] EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2406.14075 (cs) [Submitted on 20 Jun 2024 ( v1 ), last revised 9 Jun 2026 (this version, v3)] Title: EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain Authors: Yi-Fan Lu , Xian-Ling Mao , Bo Wang , Xiao Liu , Heyan Huang View a PDF of the paper titled EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain, by Yi-Fan Lu and 4 other authors View PDF HTML (experimental) 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. Comments: Accepted by ACL 2026 Main Conference, Oral Subjects: Computation and Language (cs.CL) Cite as: arXiv:2406.14075 [cs.CL] (or arXiv:2406.14075v3 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2406.14075 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yi-Fan Lu [ view email ] [v1] Thu, 20 Jun 2024 07:50:37 UTC (3,694 KB) [v2] Fri, 24 Apr 2026 09:17:27 UTC (7,310 KB) [v3] Tue, 9 Jun 2026 10:01:10 UTC (7,310 KB) Full-text links: Access Paper: View a PDF of the paper titled EXCEEDS: Extracting Complex Events via Nugget-based Grid Modeling in Scientific Domain, by Yi-Fan Lu and 4 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2024-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from