GenAI for Text Summarization in German

The publishing client faced substantial costs from staff manually composing article summaries. Standard pre-trained language models fell short of the organization's quality benchmarks for German-language newspaper content.

Templefield Technologies was engaged to develop an automated mechanism for producing high-quality summaries of German newspaper articles that meet the client's editorial standards.

• Data preparation and refinement of the German-language training corpus
• Supervised Fine-Tuning (SFT) of the open-source Llama-3 model using Parameter-Efficient Fine-Tuning (PEFT) and Low-Rank Adaptation (LoRA) via Hugging Face
• Training on Nvidia A100 GPUs, with performance enhanced through quantization and memory management (flash-attention-2, gradient checkpointing)
• FastAPI-based inference delivery
• Inference refinement through parameter tuning and prompt optimization

The client deployed an economical, automated summarization platform that met its quality benchmarks — enabling workforce reallocation to higher-value editorial work and a meaningful reduction in costs. The client increased the number of articles processed by 31% without increasing headcount.