6 papers
Trailer Reimagined: An Innovative, Llm-DRiven, Expressive Automated Movie Summary framework (TRAILDREAMS)
Roberto Balestri, Pasquale Cascarano, Mirko Degli Esposti +1
This paper introduces TRAILDREAMS, a framework that uses a large language model (LLM) to automate the production of movie trailers. The purpose of LLM is to select key visual seque…
An Automatic Deep Learning Approach for Trailer Generation through Large Language Models
Roberto Balestri, Pasquale Cascarano, Mirko Degli Esposti +1
Trailers are short promotional videos designed to provide audiences with a glimpse of a movie. The process of creating a trailer typically involves selecting key scenes, dialogues…
AI Blob! LLM-Driven Recontextualization of Italian Television Archives
Roberto Balestri
This paper introduces AI Blob!, an experimental system designed to explore the potential of semantic cataloging and Large Language Models (LLMs) for the retrieval and recontextuali…
Narrative Memory in Machines: Multi-Agent Arc Extraction in Serialized TV
Roberto Balestri, Guglielmo Pescatore
Serialized television narratives present significant analytical challenges due to their complex, temporally distributed storylines that necessitate sophisticated information manage…
Gender and content bias in Large Language Models: a case study on Google Gemini 2.0 Flash Experimental
Roberto Balestri
This study evaluates the biases in Gemini 2.0 Flash Experimental, a state-of-the-art large language model (LLM) developed by Google, focusing on content moderation and gender dispa…
Multi-Agent System for AI-Assisted Extraction of Narrative Arcs in TV Series
Roberto Balestri, Guglielmo Pescatore
Serialized TV shows are built on complex storylines that can be hard to track and evolve in ways that defy straightforward analysis. This paper introduces a multi-agent system desi…