Selecting which claims to check is a time-consuming task for human
fact-checkers, especially from documents consisting of multiple sentences and
containing multiple claims. However, existing claim extraction approaches focus
more on identifying and extracting claims from individual sentences, e.g.,
identifying whether a sentence contains a claim or the exact boundaries of the
claim within a sentence. In this paper, we propose a method for document-level
claim extraction for fact-checking, which aims to extract check-worthy claims
from documents and decontextualise them so that they can be understood out of
context. Specifically, we first recast claim extraction as extractive
summarization in order to identify central sentences from documents, then
rewrite them to include necessary context from the originating document through
sentence decontextualisation. Evaluation with both automatic metrics and a
fact-checking professional shows that our method is able to extract
check-worthy claims from documents more accurately than previous work, while
also improving evidence retrieval.

我们提出了一种用于事实检查的文档级索取索赔的方法，旨在从文档中提取值得检查的要求，并将它们解除背景以便在任何环境下理解。使用自动评估指标和事实检查专业人员的评估结果表明，我们的方法比以前的工作更准确地从文档中提取出有价值的索赔，并改善了证据的检索。

文档级主张提取与脱离背景化以进行事实核查

Document-level Claim Extraction and Decontextualisation for  Fact-Checking

Traditionally, a debate usually requires a manual preparation process,
including reading plenty of articles, selecting the claims, identifying the
stances of the claims, seeking the evidence for the claims, etc. As the AI
debate attracts more attention these years, it is worth exploring the methods
to automate the tedious process involved in the debating system. In this work,
we introduce a comprehensive and large dataset named IAM, which can be applied
to a series of argument mining tasks, including claim extraction, stance
classification, evidence extraction, etc. Our dataset is collected from over 1k
articles related to 123 topics. Near 70k sentences in the dataset are fully
annotated based on their argument properties (e.g., claims, stances, evidence,
etc.). We further propose two new integrated argument mining tasks associated
with the debate preparation process: (1) claim extraction with stance
classification (CESC) and (2) claim-evidence pair extraction (CEPE). We adopt a
pipeline approach and an end-to-end method for each integrated task separately.
Promising experimental results are reported to show the values and challenges
of our proposed tasks, and motivate future research on argument mining.

本文提出了一个全面而大型的数据集 IAM，旨在用于一系列论证挖掘任务。文集介绍了两个新的综合论证挖掘任务，即主张提取与立场分类（CESC）以及主张 - 证据对提取（CEPE）。实验结果表明了我们提出的任务的价值和挑战，并激励了论证挖掘领域的未来研究。

IAM：一份全面的大规模数据集，用于集成化论证挖掘任务

IAM: A Comprehensive and Large-Scale Dataset for Integrated Argument Mining Tasks

Claims are a fundamental unit of scientific discourse. The exponential growth
in the number of scientific publications makes automatic claim extraction an
important problem for researchers who are overwhelmed by this information
overload. Such an automated claim extraction system is useful for both manual
and programmatic exploration of scientific knowledge. In this paper, we
introduce a new dataset of 1,500 scientific abstracts from the biomedical
domain with expert annotations for each sentence indicating whether the
sentence presents a scientific claim. We introduce a new model for claim
extraction and compare it to several baseline models including rule-based and
deep learning techniques. Moreover, we show that using a transfer learning
approach with a fine-tuning step allows us to improve performance from a large
discourse-annotated dataset. Our final model increases F1-score by over 14
percent points compared to a baseline model without transfer learning. We
release a publicly accessible tool for discourse and claims prediction along
with an annotation tool. We discuss further applications beyond biomedical
literature.

本文介绍了一种用于从生物医学领域的科学文章摘要中提取科学主张的模型，该模型使用传递学习技术进行微调，并将其与基于规则和深度学习技术的几个基准模型进行比较。最终模型使用微调后的传递学习技术与基准模型相比，其 F1 得分提高了 14 个百分点。同时，作者还提供了一个公共工具用于进行论述和主张的预测以及注释工具，并探讨了其在生物医学文献以外的其他应用。