@inproceedings{steel-ruths-2025-corpus,
title = "Corpus-Oriented Stance Target Extraction",
author = "Steel, Benjamin and
Ruths, Derek",
editor = "Strube, Michael and
Braud, Chloe and
Hardmeier, Christian and
Li, Junyi Jessy and
Loaiciga, Sharid and
Zeldes, Amir and
Li, Chuyuan",
booktitle = "Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.codi-1.18/",
pages = "209--227",
ISBN = "979-8-89176-343-2",
abstract = "Understanding public discourse through the frame of stance detection requires effective extraction of issues of discussion, or stance targets. Yet current approaches to stance target extraction are limited, only focusing on a single document to single stance target mapping. We propose a broader view of stance target extraction, which we call corpus-oriented stance target extraction. This approach considers that documents have multiple stance targets, those stance targets are hierarchical in nature, and document stance targets should not be considered in isolation of other documents in a corpus. We develop a formalization and metrics for this task, propose a new method to address this task, and show its improvement over previous methods using supervised and unsupervised metrics, and human evaluation tasks. Finally, we demonstrate its utility in a case study, showcasing its ability to aid in reliably surfacing key issues of discussion in large-scale corpuses."
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<abstract>Understanding public discourse through the frame of stance detection requires effective extraction of issues of discussion, or stance targets. Yet current approaches to stance target extraction are limited, only focusing on a single document to single stance target mapping. We propose a broader view of stance target extraction, which we call corpus-oriented stance target extraction. This approach considers that documents have multiple stance targets, those stance targets are hierarchical in nature, and document stance targets should not be considered in isolation of other documents in a corpus. We develop a formalization and metrics for this task, propose a new method to address this task, and show its improvement over previous methods using supervised and unsupervised metrics, and human evaluation tasks. Finally, we demonstrate its utility in a case study, showcasing its ability to aid in reliably surfacing key issues of discussion in large-scale corpuses.</abstract>
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%0 Conference Proceedings
%T Corpus-Oriented Stance Target Extraction
%A Steel, Benjamin
%A Ruths, Derek
%Y Strube, Michael
%Y Braud, Chloe
%Y Hardmeier, Christian
%Y Li, Junyi Jessy
%Y Loaiciga, Sharid
%Y Zeldes, Amir
%Y Li, Chuyuan
%S Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025)
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-343-2
%F steel-ruths-2025-corpus
%X Understanding public discourse through the frame of stance detection requires effective extraction of issues of discussion, or stance targets. Yet current approaches to stance target extraction are limited, only focusing on a single document to single stance target mapping. We propose a broader view of stance target extraction, which we call corpus-oriented stance target extraction. This approach considers that documents have multiple stance targets, those stance targets are hierarchical in nature, and document stance targets should not be considered in isolation of other documents in a corpus. We develop a formalization and metrics for this task, propose a new method to address this task, and show its improvement over previous methods using supervised and unsupervised metrics, and human evaluation tasks. Finally, we demonstrate its utility in a case study, showcasing its ability to aid in reliably surfacing key issues of discussion in large-scale corpuses.
%U https://aclanthology.org/2025.codi-1.18/
%P 209-227
Markdown (Informal)
[Corpus-Oriented Stance Target Extraction](https://aclanthology.org/2025.codi-1.18/) (Steel & Ruths, CODI 2025)
ACL
- Benjamin Steel and Derek Ruths. 2025. Corpus-Oriented Stance Target Extraction. In Proceedings of the 6th Workshop on Computational Approaches to Discourse, Context and Document-Level Inferences (CODI 2025), pages 209–227, Suzhou, China. Association for Computational Linguistics.