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    <abstract>Corporate environmental claims frequently appear in news articles, providing an external view of sustainability-related communications. However, extracting such claims remains challenging as they are often implicit, distributed across multiple sentences, and embedded within narrative contexts. In this work, we propose an end-to-end system for automated environmental claim extraction enriched with contextual metadata. Our pipeline combines multi-label topic classification, topic-conditioned candidate claim-evidence extraction using large language models (LLMs), and candidate filtering with ASE-BERT, a lightweight aspect- and sentiment-aware classifier for claim detection. To support evaluation, we construct a curated dataset of corporate environmental news, annotated with claims, supporting evidence, and contextual attributes. Experimental results demonstrate that for claim classification, ASE-BERT attains the highest F1 (0.87) across the four combined environmental claim datasets, exceeding the best baseline without task-specific fine-tuning by 4 points. For end-to-end extraction, topic conditioning alone improves LLM F1 by an average of 15 points, and our full pipeline adds a further 5 points over the best topic-conditioned setting. These findings demonstrate that incorporating topic awareness and claim filtering substantially improves environmental claim extraction from corporate news, supporting downstream sustainability intelligence and decision-making tasks.</abstract>
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%0 Conference Proceedings
%T Topic-Aware Environmental Claim Extraction from Corporate News
%A Gupta, Tanay Kumar
%A Aggarwal, Vaishali
%A Verma, Ishan
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F gupta-etal-2026-topic
%X Corporate environmental claims frequently appear in news articles, providing an external view of sustainability-related communications. However, extracting such claims remains challenging as they are often implicit, distributed across multiple sentences, and embedded within narrative contexts. In this work, we propose an end-to-end system for automated environmental claim extraction enriched with contextual metadata. Our pipeline combines multi-label topic classification, topic-conditioned candidate claim-evidence extraction using large language models (LLMs), and candidate filtering with ASE-BERT, a lightweight aspect- and sentiment-aware classifier for claim detection. To support evaluation, we construct a curated dataset of corporate environmental news, annotated with claims, supporting evidence, and contextual attributes. Experimental results demonstrate that for claim classification, ASE-BERT attains the highest F1 (0.87) across the four combined environmental claim datasets, exceeding the best baseline without task-specific fine-tuning by 4 points. For end-to-end extraction, topic conditioning alone improves LLM F1 by an average of 15 points, and our full pipeline adds a further 5 points over the best topic-conditioned setting. These findings demonstrate that incorporating topic awareness and claim filtering substantially improves environmental claim extraction from corporate news, supporting downstream sustainability intelligence and decision-making tasks.
%U https://aclanthology.org/2026.finnlp-1.53/
%P 632-649

Markdown (Informal)

[Topic-Aware Environmental Claim Extraction from Corporate News](https://aclanthology.org/2026.finnlp-1.53/) (Gupta et al., FinNLP 2026)

• Topic-Aware Environmental Claim Extraction from Corporate News (Gupta et al., FinNLP 2026)

ACL

• Tanay Kumar Gupta, Vaishali Aggarwal, and Ishan Verma. 2026. Topic-Aware Environmental Claim Extraction from Corporate News. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 632–649, Budapest, Hungary. Association for Computational Linguistics.