Daniel O. Cajueiro

Daniel O. Cajueiro, Arthur G. Nery, Igor Tavares et al.

We provide a literature review about Automatic Text Summarization (ATS) systems. We consider a citation-based approach. We start with some popular and well-known papers that we have in hand about each topic we want to cover and we have tracked the "backward citations" (papers that are cited by the set of papers we knew beforehand) and the "forward citations" (newer papers that cite the set of papers we knew beforehand). In order to organize the different methods, we present the diverse approaches to ATS guided by the mechanisms they use to generate a summary. Besides presenting the methods, we also present an extensive review of the datasets available for summarization tasks and the methods used to evaluate the quality of the summaries. Finally, we present an empirical exploration of these methods using the CNN Corpus dataset that provides golden summaries for extractive and abstractive methods.

Nuno Crokidakis, Marcio Argollo de Menezes, Daniel O. Cajueiro

We present an overview of the complex systems field using ChatGPT as a representation of the community's understanding. ChatGPT has learned language patterns and styles from a large dataset of internet texts, allowing it to provide answers that reflect common opinions, ideas, and language patterns found in the community. Our exploration covers both teaching and learning, and research topics. We recognize the value of ChatGPT as a source for the community's ideas.

Vítor Bandeira Borges, Daniel Oliveira Cajueiro

In this study, we delve into the dynamic landscape of machine learning research evolution. Initially, through the utilization of Latent Dirichlet Allocation, we discern pivotal themes and fundamental concepts that have emerged within the realm of machine learning. Subsequently, we undertake a comprehensive analysis to track the evolutionary trajectories of these identified themes. To quantify the novelty and divergence of research contributions, we employ the Kullback-Leibler Divergence metric. This statistical measure serves as a proxy for ``surprise'', indicating the extent of differentiation between the content of academic papers and the subsequent developments in research. By amalgamating these insights, we gain the ability to ascertain the pivotal roles played by prominent researchers and the significance of specific academic venues (periodicals and conferences) within the machine learning domain.