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.

提出了一种混合（人 + AI）方法 HyEnA，用于从具有倾向性的文本中提取论点，结合了自动处理速度和人类的理解和推理能力。在三个市民反馈文献中评估了 HyEnA，发现它相对于多样的观点集合，既提供了更高的覆盖率和精确度，强调了人类洞察力的必要性，同时又减少了人力成本，与（完全手动的）专家分析相比并没有降低质量，从而展示了结合人工和人工智能的优势。