Many thanks to Philipp Heinisch, Janosch Gehring, Philipp Cimiano and @anettemfrank.bsky.social (@hd-nlp.bsky.social) for their great contributions.

Please reach out if you have any questions, and see you in Albuquerque at @naaclmeeting.bsky.social 2025 🥳

For example, we find signature perspectives that authors tend to agree, disagree, or be orthogonal on. Many more details, considering e.g., different stakeholder groups are in the paper.

We propose methods for all three subtasks and evaluate them individually using human annotations and automated metrics.
After validating our approach, we conduct a case study on real-world data, demonstrating how PSVs analyze debates with a focus on deliberative resolutions.

3️⃣ Finally, we aggregate PSVs to compute (dis)agreement scores, both for individual perspectives and overall argument alignment. E.g., the two example arguments agree on “trophy hunting” but disagree on “hunting for food”. For “eating meat” the arguments are orthogonal.

2️⃣ Each argument is then mapped to a Perspectivized Stance Vector (PSV), a structured representation that captures the stance (✅❔❌) toward each perspective.

Our approach consists of three steps:

1️⃣ For a given debate topic, we identify signature perspectives that represent key viewpoints.

Consider the image above. Hunting for food, trophy hunting, eating meat and sustainability are the signature perspectives for discussed topic.