This study explores the overlap between text summarization and simplification outputs. While summarization evaluation methods are streamlined, simplification lacks cohesion, prompting the question: how closely can abstractive summarization resemble gold-standard simplification? We address this by applying two BART-based BRIO summarization methods to the Newsela corpus, comparing outputs with manually annotated simplifications and achieving a top ROUGE-L score of 0.654. This provides insight into where summarization and simplification outputs converge and differ.

The search for formal causal relations in natural language faces inherent limitations due to the lack of mathematically and logically informed datasets. Thus, the exploration of causal relations in natural language leads to the analysis of formal-logic-adjacent language patterns. Thanks to the recent advancements of generative LLMs, this research niche is expanding within the field of natural language processing and evaluation. In this work, we conduct an evaluation of 9 models produced by different AI developing companies in order to answer the question “Are LLMs capable of discerning between different types of causal relations?”. The SciExpl dataset is chosen as a natural language corpus, and we develop three different prompt types aligned with zero-shot, few-shot, and chain-of-thought standards to evaluate the performance of the LLMs. Claude 3.7 Sonnet and Gemini 2.5 Flash Preview emerge as the best models for the task, with the respective highest F1 scores of 0.842 (few-shot prompting) and 0.846 (chain-of-thought prompting).