14 Concept Maps are semantic graph summary representations of relations between concepts in text. They are particularly beneficial for students with difficulty in reading comprehension, such as those with special educational needs and disabilities. Currently, the field of concept map extraction from text is outdated, relying on old baselines, limited datasets, and limited performances with F1 scores below 20%. We propose a novel neuro-symbolic pipeline and a GPT3.5-based method for automated concept map extraction from text evaluated over the WIKI dataset. The pipeline is a robust, modularized, and open-source architecture, the first to use semantic and neural techniques for automatic concept map extraction while also using a preliminary summarization component to reduce processing time and optimize computational resources. Furthermore, we investigate the large language model in zero-shot, one-shot, and decomposed prompting for concept map generation. Our approaches achieve state-of-the-art results in METEOR metrics, with F1 scores of 25.7 and 28.5, respectively, and in ROUGE-2 recall, with respective scores of 24.3 and 24.3. This contribution advances the task of automated concept map extraction from text, opening doors to wider applications such as education and speech-language therapy. The code is openly available.

Accurate song success prediction is vital for the music industry, guiding promotion and label decisions. Early, accurate predictions are thus crucial for informed business actions. We investigated the predictive power of lyrics embedding features, alone and in combination with other stylometric features and various Spotify metadata (audio, platform, playlists, reactions). We compiled a dataset of 12,428 Spotify tracks and targeted popularity 15 days post-release. For the embeddings, we used a Large Language Model and compared different configurations. We found that integrating embeddings with other lyrics and audio features improved early-phase predictions, underscoring the importance of a comprehensive approach to success prediction.