Large-scale survey tools enable the collection of citizen feedback in opinion corpora. Extracting the key arguments from a large and noisy set of opinions helps in understanding the opinions quickly and accurately. Fully automated methods can extract arguments but (1) require large labeled datasets that induce large annotation costs and (2) work well for known viewpoints, but not for novel points of view. We propose HyEnA, a hybrid (human + AI) method for extracting arguments from opinionated texts, combining the speed of automated processing with the understanding and reasoning capabilities of humans. We evaluate HyEnA on three citizen feedback corpora. We find that, on the one hand, HyEnA achieves higher coverage and precision than a state-of-the-art automated method when compared to a common set of diverse opinions, justifying the need for human insight. On the other hand, HyEnA requires less human effort and does not compromise quality compared to (fully manual) expert analysis, demonstrating the benefit of combining human and artificial intelligence.

翻译：大规模调查工具能够收集公民反馈意见语料库。从大量且嘈杂的意见中提取关键论点，有助于快速准确地理解民众意见。全自动化方法可以提取论点，但存在两个问题：（1）需要大规模标注数据集，导致高昂的标注成本；（2）对已知观点表现良好，但对新颖观点效果不佳。我们提出HyEnA——一种从观点文本中提取论点的混合（人类+人工智能）方法，将自动化处理的速度与人类的理解与推理能力相结合。我们在三个公民反馈语料库上评估了HyEnA。研究发现：一方面，与一套多样化观点的共同基准相比，HyEnA在覆盖率和精确度上均优于最先进的自动化方法，这论证了对人类洞察力的需求；另一方面，与（全人工）专家分析相比，HyEnA在减少人力投入的同时并未牺牲质量，彰显了人类智能与人工智能协同的优势。