为议论文生成信息性结论
Generating Informative Conclusions for Argumentative Texts
摘要：
议论文的目的是支持某一结论。然而，他们往往被省略，期待读者推断他们。虽然在阅读单个文本时是适当的，但这种修辞手段在浏览许多文本时限制了可访问性（例如，在搜索引擎上或在社交媒体上）。在这些情况下，一个明确的结论是一个很好的候选摘要的论点。如果结论是信息性的，强调文本中的具体概念，这一点尤其正确。本文介绍了生成信息性结论的任务：首先，编制了Webis-ConcluGen-21，一个包含136，996个议论文及其结论样本的大规模语料库。其次，两种范式的结论生成的研究;一个提取，其他抽象的性质。后者利用论证知识，通过控制代码和微调的BART模型的语料库的几个子集，增加数据。第三，洞察我们的语料库的任务，两代范式之间的差异，信息量和简洁性之间的权衡，以及编码的争论性知识的影响的适用性提供。语料库、代码和训练模型都是公开的。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
