Details
| Original language | English |
|---|---|
| Title of host publication | Findings of the Association for Computational Linguistics |
| Subtitle of host publication | ACL-IJCNLP 2021 |
| Editors | Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli |
| Pages | 3482-3493 |
| Number of pages | 12 |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 - Virtual, Online Duration: 1 Aug 2021 → 6 Aug 2021 |
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.
ASJC Scopus subject areas
- Arts and Humanities(all)
- Language and Linguistics
- Social Sciences(all)
- Linguistics and Language
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Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021. ed. / Chengqing Zong; Fei Xia; Wenjie Li; Roberto Navigli. 2021. p. 3482-3493.
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Generating Informative Conclusions for Argumentative Texts
AU - Syed, Shahbaz
AU - Al-Khatib, Khalid
AU - Alshomary, Milad
AU - Wachsmuth, Henning
AU - Potthast, Martin
N1 - Funding Information: We thank the reviewers for their valuable feedback. This work was supported by the German Federal Ministry of Education and Research (BMBF, 01/S18026A-F) by funding the competence center for Big Data and AI (ScaDS.AI Dresden/Leipzig). Computations for this work were done (in part) using resources of the Leipzig University Computing Centre, who we sincerely thank for their support.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85122427779&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2106.01064
DO - 10.48550/arXiv.2106.01064
M3 - Conference contribution
AN - SCOPUS:85122427779
SN - 9781954085541
SP - 3482
EP - 3493
BT - Findings of the Association for Computational Linguistics
A2 - Zong, Chengqing
A2 - Xia, Fei
A2 - Li, Wenjie
A2 - Navigli, Roberto
T2 - Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Y2 - 1 August 2021 through 6 August 2021
ER -