Conceptually, an argument logically combines a claim with a set of reasons. In real-world text, however, arguments may be spread over several sentences, often intertwine multiple claims and reasons along with context information and rhetorical devices, and are inherently subjective. This project aims to study how to computationally obtain an objective summary of the gist of an argumentative text. In particular, we aim to establish foundations of natural language processing methods that (1) analyze the gist of an argument's reasoning, (2) generate a text snippet that summarizes the gist concisely, and (3) neutralize potential subjective bias in the summary as far as possible.
The rationale of the DFG-funded project is that argumentation machines, as envisioned by the RATIO priority program (SPP 1999), are meant to present the different positions people may have towards controversial issues, such as abortion or social distancing. One prototypical machine is our argument search engine, args.me, which opposes pro and con arguments from the web in response to user queries, in order to support self-determined opinion formation. A key aspect of args.me and comparable machines is to generate argument snippets, which give the user an efficient overview of the usually manifold arguments. Standard snippet generation has turned out to be insufficient for this purpose. We hypothesize that the best argument snippet summarizes the argument's gist objectively.
More information about the priority program can be found on the RATIO website.