  The purpose of an argumentative text is to support a certain conclusion. Yet,
they are often omitted, expecting readers to infer them rather. While
appropriate when reading an individual text, this rhetorical device limits
accessibility when browsing many texts (e.g., on a search engine or on social
media). In these scenarios, an explicit conclusion makes for a good candidate
summary of an argumentative text. This is especially true if the conclusion is
informative, emphasizing specific concepts from the text. With this paper we
introduce the task of generating informative conclusions: First,
Webis-ConcluGen-21 is compiled, a large-scale corpus of 136,996 samples of
argumentative texts and their conclusions. Second, two paradigms for conclusion
generation are investigated; one extractive, the other abstractive in nature.
The latter exploits argumentative knowledge that augment the data via control
codes and finetuning the BART model on several subsets of the corpus. Third,
insights are provided into the suitability of our corpus for the task, the
differences between the two generation paradigms, the trade-off between
informativeness and conciseness, and the impact of encoding argumentative
knowledge. The corpus, code, and the trained models are publicly available.
