added tldr functionality to blog posts, backfilled this to existing posts

Author: tobideusser

_posts/2026-04-15-welcome-to-the-aml-lab-blog.md

@@ -5,6 +5,7 @@ date: 2026-04-15
 author: Tobias Deußer
 categories: [News]
 description: false
+tldr: "Welcome to the official blog of the Applied Machine Learning Lab at the University of Bonn: our space for research highlights, course announcements, lab news, and tutorials."
 ---
 
 Welcome to the official blog of the Applied Machine Learning Lab at the University of Bonn! This space is where we share updates about our research, courses, events, and insights from our work in machine learning.

_posts/2026-04-16-Towards-Automated-Recipe-Reconstruction.md

@@ -5,6 +5,7 @@ date: 2026-04-16
 author: Svetlana Schmidt
 categories: [Research]
 description: false
+tldr: "To automate the labor-intensive expansion of nutritional databases, we built a two-stage information retrieval system that matches food items by text and nutrient similarity. Adding SVM-based food category prediction (99% accuracy) boosted retrieval precision to 80%, and we outline an LLM-plus-optimization pipeline for simulating unmatched recipes."
 ---
 
 This is a short summary of our paper **"Towards Automated Recipe Reconstruction: Optimization of Dietary Data Collection using Information Retrieval, Large Language Models and Mathematical Optimization"** by *Svetlana Schmidt, Linda Klasen, Ute Nöthlings, and Rafet Sifa* published in the proceedings of the 2025 IEEE International Conference on Big Data. See [here](https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/14070) for the full paper.

_posts/2026-04-29-leveraging-synthetically-generated-data-for-real-estate-document-classification.md

@@ -5,6 +5,7 @@ date: 2026-04-29
 author: Tobias Deußer
 categories: [Research]
 description: false
+tldr: "Sensitive documents make collecting real training data hard, so we generate realistic synthetic data instead: template-based documents filled by Faker and LLMs. A BERT classifier trained purely on synthetic data reaches 88% page-wise precision on a real-world test set it never saw during training."
 ---
 
 This is a short summary of our paper **"Leveraging Synthetically Generated Data for Real Estate Document Classification"** by *Tobias Deußer, Gregor Ramien, Nico Weber, Maximilian Meidinger, Max Hahnbück, Christian Bauckhage, and Rafet Sifa*, published in the proceedings of the 2025 IEEE International Conference on Big Data. See [here](https://ieeexplore.ieee.org/abstract/document/11400789) for the published version and [here](https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/13972) for the open-access bonndoc version.

_posts/2026-05-22-When-RL-Wins-the-Benchmark-but-Loses-the-Patient.md

@@ -5,6 +5,7 @@ date: 2026-05-22
 author: Armin Berger
 categories: [Research]
 description: false
+tldr: "RL fine-tuning (GRPO) lifted our chest X-ray model's score 23% on one benchmark but dropped it 19% on another: it learned to predict dataset-specific labels, not to read X-rays. The same pattern appears at 50x the budget, so the problem is the recipe, not the resources. Carefully curated SFT generalized better across institutions."
 ---
 
 This is a TL;DR of our paper **"Benchmark Success, Clinical Failure: When Reinforcement Learning Optimizes for Benchmarks, Not Patients"** by *Armin Berger, Manuela Bergau, Helen Schneider, Saad Ahmad, Tom Anglim Lagones, Gianluca Brugnara, Martha Foltyn-Dumitru, Kai Schlamp, Philipp Vollmuth, and Rafet Sifa*, available on [arXiv](https://arxiv.org/abs/2512.23090) and to be published in the Proceedings of IJCNN 2026.

_posts/2026-05-28-From-Retinal-Pixels-to-Patients.md

@@ -5,6 +5,7 @@ date: 2026-05-28
 author: Muskaan Chopra
 categories: [Research]
 description: false
+tldr: "A survey of deep learning for diabetic retinopathy screening (2016–2025), spanning 50+ studies and 20+ datasets. The key takeaway: strong benchmark scores don't equal clinical readiness: the field needs reproducible code, external validation, calibration, and per-patient evaluation."
 ---
 
 This is a short summary of our paper **"From Retinal Pixels to Patients: Evolution of Deep Learning Research in Diabetic Retinopathy Screening"** by *Muskaan Chopra, Lorenz Sparrenberg, Armin Berger, Sarthak Khanna, Jan H. Terheyden, and Rafet Sifa*, published in the proceedings of the 2025 IEEE International Conference on Big Data. See [here](https://ieeexplore.ieee.org/document/11402603) for the published version and [here](https://arxiv.org/abs/2511.11065) for the open-access arxiv version.

index.md

@@ -28,7 +28,7 @@ If you want to reach out to us, please write to:
     {% endif %}
   </div>
   <div class="post-excerpt">
-    {{ latest.excerpt | strip_html | truncatewords: 60 }}
+    {% if latest.tldr %}{{ latest.tldr }}{% else %}{{ latest.excerpt | strip_html | truncatewords: 60 }}{% endif %}
   </div>
   <a href="{{ latest.url | relative_url }}" class="read-more-link">Read more &rarr;</a>
 </article>

news/index.html

@@ -16,7 +16,7 @@ <h2 class="post-card-title"><a href="{{ post.url | relative_url }}">{{ post.titl
       {% endif %}
     </div>
     <div class="post-excerpt">
-      {{ post.excerpt | strip_html | truncatewords: 60 }}
+      {% if post.tldr %}{{ post.tldr }}{% else %}{{ post.excerpt | strip_html | truncatewords: 60 }}{% endif %}
     </div>
     <a href="{{ post.url | relative_url }}" class="read-more-link">Read more &rarr;</a>
   </article>