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The technology mines and clusters arguments from a\u00a0variety of textual sources for a\u00a0large 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\u00a0focus 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.<\/jats:p>","DOI":"10.1007\/s13222-020-00347-7","type":"journal-article","created":{"date-parts":[[2020,6,16]],"date-time":"2020-06-16T12:02:35Z","timestamp":1592308955000},"page":"115-121","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["ArgumenText: Argument Classification and Clustering in a\u00a0Generalized Search Scenario"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7385-5654","authenticated-orcid":false,"given":"Johannes","family":"Daxenberger","sequence":"first","affiliation":[]},{"given":"Benjamin","family":"Schiller","sequence":"additional","affiliation":[]},{"given":"Chris","family":"Stahlhut","sequence":"additional","affiliation":[]},{"given":"Erik","family":"Kaiser","sequence":"additional","affiliation":[]},{"given":"Iryna","family":"Gurevych","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,6,16]]},"reference":[{"key":"347_CR1","doi-asserted-by":"publisher","first-page":"64","DOI":"10.3115\/v1\/W14-2109","volume-title":"ArgMining@ACL\u201914","author":"E Aharoni","year":"2014","unstructured":"Aharoni E, Polnarov A, Lavee T, Hershcovich D, Levy R, Rinott R, Gutfreund D, Slonim N (2014) A\u00a0benchmark dataset for automatic detection of claims and evidence in the context of controversial topics. 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