Evaluating Unsupervised Argument Aligners via Generation of Conclusions of Structured Scientific Abstracts

AbstractScientific abstracts provide a concise summary of research findings, making them a valuable resource for extracting scientific arguments. In this study, we assess various unsupervised approaches for extracting arguments as aligned premise-conclusion pairs: semantic similarity, text perplexity, and mutual information. We aggregate structured abstracts from PubMed Central Open Access papers published in 2022 and evaluate the argument aligners in terms of the performance of language models that we fine-tune to generate the conclusions from the extracted premise given as input prompts. We find that mutual information outperforms the other measures on this task, suggesting that the reasoning process in scientific abstracts hinges mostly on linguistic constructs beyond simple textual similarity.