Generating informative conclusions for argumentative texts
Year: 2021
Type: preprint
Abstract: 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. 1
Authors: Syed, Shahbaz, Al-Khatib, Khalid, Alshomary, Milad, Wachsmuth, Henning, Potthast, Martin
Institutions: Paderborn University, Leipzig University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3177437287