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。