Generating Informative Conclusions for Argumentative Texts

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

Assessing the Sufficiency of Arguments through Conclusion Generation

“…Results Table 1 lists the results of the approaches. BART-unsupervised is a strong baseline in terms of lexical accuracy: Values such as 19.69 (ROUGE-1) and 16.40 (ROUGE-L) are comparable to those that Syed et al (2021) achieved in similar domains with sophisticated approaches. However, finetuning on the argument-argument pairs does not only significantly increase the semantic similarity between generated and ground truth conclusions from 0.14 to 0.25 in terms of BERTScore, but it also leads to a slight increase in lexical accuracy.…”

“…The idea of reconstructing an argument's conclusion from its premises was introduced by Alshomary et al ( 2020), but their approach focused on the inference of a conclusion's target. The actual generation of entire conclusions has so far only been studied by Syed et al (2021). The authors presented the first corpus for this task along with experiments where they adapted BART (Lewis et al, 2020) from summarization to conclusion generation.…”

“…This question, finally, referred to the novelty of the generated conclusions in light of their context. The score range here is more complex; inspired by Syed et al (2021), who also ask annotators about the novelty of generated conclusions, it reflects the cognitive load required to infer the conclusions from the context of the argument: "verbatim copying" (1), "synonymous copying" (2), "copying + fusion" (3), "inference" (4), and "can not be composed" (5).…”

DeepA2: A Modular Framework for Deep Argument Analysis with Pretrained Neural Text2Text Language Models

“…Akiki and Potthast (2020) explore abstractive argument retrieval by means of text generation with GPT2 (Radford et al 2019). Similarly, Syed et al (2021) deploy BART (Lewis et al 2019) to generate conclusions of argumentative texts on a challenging corpus compiled from Reddit and various online debate corpora. Rodrigues et al (2020), revisiting the argument comprehension task (Habernal, Eckle-Kohler, and Gurevych 2014;Habernal et al 2018), demonstrate that identifying implicit premises -and deep argument analysis a fortiori -remains a hard, unsolved task.…”

Key Point Analysis via Contrastive Learning and Extractive Argument Summarization

“…Wang and Ling (2016) used a sequence-to-sequence model for the abstractive summarization of arguments from online debate portals. A complementary task of generating conclusions as informative argument summaries was introduced by Syed et al (2021). Similar to Alshomary et al (2020b) who inferred a conclusion's target with a triplet neural network, we rely on contrastive learning here, using a siamese network though.…”