A senior undergrad built an automated MLOps pipeline for AI news classification and summarization as their thesis project. They are seeking feedback on their current setup, which leverages data scraping, classification, and the Gemini API for content summarization.
![Need feedback on my Senior Thesis: An automated MLOps pipeline for AI news classification & summarization [D]](/cdn-cgi/image/width=3840,quality=75,format=webp/https://preview.redd.it/wqv7mg1e1kvg1.png?width=140&height=77&auto=webp&s=03d8f38014e24bd3ba9837868280bafea56bb1b8)
The author trained Qwen2.5-0.5B-Instruct for Reddit post summarization using two reward strategies, finding that a combination of quality and length penalties yielded significantly better results. Evaluation was conducted using LLM-As-A-Judge and DeepEval tools for metrics like conscientiousness and clarity.
The author built an AI text summarizer in one night using Claude and Next.js, deploying a functional demo. The tool offers various summary styles like concise, bullet points, executive summary, or ELI5, providing quick results.
This educational article will discuss AI automation for non-fiction ghostwriters, specifically covering interview transcript summarization and chapter outline creation. It will explain how to structure narratives from themes, detailing chapter goals, narrative arcs, and steps, incorporating a specific AI tool.
O texto aborda como sistemas de IA perdem significado institucional e situacional ao resumir informações, devido à fragmentação de conteúdo que desvincula metadados estruturais cruciais. Isso resulta em respostas geradas pela IA que são confiantes, mas imprecisas, por falta de contexto.
Independent video editors face the challenge of extracting stories from vast amounts of raw footage and transcripts. This article proposes a two-tiered AI workflow to automate summarization and clip selection, treating AI as a collaborative story editor.
This paper proposes an evidence-based model to generate queries from query-free summarization datasets, addressing the challenge of finding suitable datasets for Query-Focused Summarization (QFS). Experimental results indicate that summaries generated using these evidence-based queries achieve competitive ROUGE scores, supporting their effectiveness for the QFS task.
S^2tory is a narratology-grounded AI framework designed for movie script summarization, addressing the complexity of non-linear narratives by identifying "plot nuclei" through character development trajectories. It employs a Narrative Expert Agent to distill knowledge, which then conditions a model to identify essential plot points for summary generation.