In this paper, we introduce an expert-annotated dataset for detecting real-world environmental claims made by listed companies.
We argue that this deficiency is one of the reasons why very few publications in NLP report key figures that would allow a more thorough examination of environmental impact.
Over the recent years, large pretrained language models (LM) have revolutionized the field of natural language processing (NLP).
Climate change communication in the mass media and other textual sources may affect and shape public perception.
We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims.
SUMO further generates an extractive summary by presenting a diversified set of sentences from the documents that explain its decision on the correctness of the textual claim.
On the other hand, more recent works that use headline guided attention to learn a headline derived contextual representation of the news body also result in convoluting overall representation due to the news body's lengthiness.