Generating Informative Conclusions for Argumentative Texts

verfasst von

Shahbaz Syed, Khalid Al-Khatib, Milad Alshomary, Henning Wachsmuth, Martin Potthast

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, WebisConcluGen-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.

Externe Organisation(en)

Universität Leipzig
Universität Paderborn

Typ

Aufsatz in Konferenzband

Seiten

3482-3493

Anzahl der Seiten

12

Publikationsdatum

2021

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Sprache und Linguistik, Linguistik und Sprache

Elektronische Version(en)

https://doi.org/10.48550/arXiv.2106.01064 (Zugang: Offen)
https://doi.org/10.18653/v1/2021.findings-acl.306 (Zugang: Offen)