Daxenberger, JohannesSchiller, BenjaminStahlhut, ChrisKaiser, ErikGurevych, Iryna2021-05-042021-05-0420202020http://dx.doi.org/10.1007/s13222-020-00347-7https://dl.gi.de/handle/20.500.12116/36394The ArgumenText project creates argument mining technology for big and heterogeneous data and aims to evaluate its use in real-world applications. The technology mines and clusters arguments from a variety of textual sources for a large range of topics and in multiple languages. Its main strength is its generalization to very different textual sources including web crawls, news data, or customer reviews. We validated the technology with a focus on supporting decisions in innovation management as well as customer feedback analysis. Along with its public argument search engine and API, ArgumenText has released multiple datasets for argument classification and clustering. This contribution outlines the major technology-related challenges and proposed solutions for the tasks of argument extraction from heterogeneous sources and argument clustering. It also lays out exemplary industry applications and remaining challenges.Argument ClusteringArgument MiningText/Journal Article10.1007/s13222-020-00347-71610-1995