Since claims stand at a clear position towards a given topic, the sentences with clear stances should have a higher possibility to be the claims. Hence, identifying the stances of the claims is supposed to benefit the claim extraction task. By combining the claim extraction and stance classification subtasks, we define this integrated task as: given a specific topic and relevant articles, extract the claims from the articles and also identify the stance of the claims towards the topic.
데이터셋
출처에 같은 태스크 이름으로 연결된 데이터셋입니다. 동명 태스크가 포함될 수 있으므로, 비교할 때는 평가 결과의 조건도 확인하세요.
We introduce a large and comprehensive dataset to facilitate the study of several essential AM tasks in the debating system. In our work, we first review the existing subtasks (claim extraction, stance classification, evidence extraction), and then propose two integrated argument mining tasks: claim extraction with stance classification (CESC) and claim-evidence pair extraction (CEPE).
Press Briefing Claim Dataset The dataset contains a total of 53 press briefings from a time span of over four years (2017-2021). While, on average, one press briefing per month is held, the distribution is highly skewed towards recent years. The Press briefings can be categorized into five main thematic categories: Climate, Energy and Mobility, Medicine, Technology and Science. The press briefings are unevenly distributed among the five categories with a major focus on medical press briefings, which also reflects the COVID-19 pandemic. In total, 177 speakers, excluding the hosts, were detected in the dataset. Most of the time the speakers are invited experts. Excluding the top 10 percentile press briefings as outliers, a press briefing had four guests on average. The press briefings consist of 3066 passages, 1719 from guests and journalists and 1294 from hosts. Regarding the sentences, 25040 sentences were collected in total, 5955 from hosts and 18942 from guests.